07_niches_SIBER_stable_isotopes
Peter Flood
2024-03-04
Copy over from dpe_iso_niche.R
1 Load libraries
library(tidyverse) #data manipulation and plotting via ggplot
library(SIBER) #modeling isotopic niches
library(openxlsx) #importing and exporting excel files
library(coda) #Bayesian model diagnostics
library(tRophicPosition) #using their pairwise comparisons functions
library(ggpubr) #plotting, arranging multiple ggplots
library(ggforce) #facet_zoom function
library(viridis) #color palettes 2 Load data
iso.merge<-read.csv(file = "Data/Stable Isotopes/isotope_merge_all.csv", header = T)3 Format data for SIBER
#prepare data for SIBER
#need to unite the habitat and season columns into a single column called "community"
iso.slough.hab.season<-unite(iso.merge, col = "community", Slough, Habitat, Season, sep ="_") %>%
mutate(size_class = case_when(
is.na(size_class) ~ Species,
!is.na(size_class) ~ size_class
))
#isolate only the data needed for SIBER
iso.siber<-select(iso.slough.hab.season, d13C, d15N, size_class, community)
colnames(iso.siber)<-c("iso1", "iso2", "group", "community")
#dropping consumers where n < 3 or sd of either isotope is 0
siber.data<-iso.siber %>%
group_by(group, community) %>%
filter(n()>2, sd(iso1)!=0, sd(iso2)!=0, !is.na(iso1), !is.na(iso2)) %>% #remove groups with n < 3 and those that didn't get C or N peaks
ungroup() %>%
as.data.frame() %>% #SIBER needs this step to work
arrange(community, group) #this step makes it so colnames() of ML output are the same order as ellipse model output
#check what was removed
removed.consumers<-iso.siber[!(iso.siber$group %in% siber.data$group),]
#createSiberObject
global.iso.siber<-createSiberObject(siber.data)Warning: At least one of your groups has less than 5 observations.
The absolute minimum sample size for each group is 3 in order
for the various ellipses and corresponding metrics to be
calculated. More reasonably though, a minimum of 5 data points
are required to calculate the two means and the 2x2 covariance
matrix and not run out of degrees of freedom. Check the item
named 'sample.sizes' in the object returned by this function
in order to locate the offending group. Bear in mind that NAs in
the sample.size matrix simply indicate groups that are not
present in that community, and is an acceptable data structure
for these analyses.#write out samples sizes table
#write.csv(global.iso.siber[["sample.sizes"]] %>% t(), file = "siber_global_iso_n_table.csv")
iso.sample.size <- global.iso.siber[["sample.sizes"]] %>% t() %>%
as.data.frame() %>% pivot_longer(cols = c(SRS_Edge_Dry:TSL_Pond_Wet),
values_to = "n", names_to = "Slough_Habitat_Season") %>%
filter(!is.na(n))
mean(iso.sample.size$n)[1] 9.975078quantile(iso.sample.size$n, c(0.025, 0.5, 0.975)) 2.5% 50% 97.5%
3 6 39 max(iso.sample.size$n)[1] 84# Create lists of plotting arguments to be passed onwards to each
# of the three plotting functions.
community.hulls.args <- list(col = 1, lty = 1, lwd = 1)
group.ellipses.args <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)
group.hull.args <- list(lty = 2, col = "grey20")
par(mfrow=c(1,1))
plotSiberObject(global.iso.siber,
ax.pad = 2,
hulls = F, community.hulls.args,
ellipses = T, group.ellipses.args,
group.hulls = T, group.hull.args,
bty = "L",
iso.order = c(1,2),
xlab = expression(d13C),
ylab = expression(d15N)
)
4 Calculate TA, SEA, SEAc
global.iso.ML <- groupMetricsML(global.iso.siber)Warning: NaNs producedprint(global.iso.ML) SRS_Edge_Dry.H.let1 SRS_Edge_Dry.L.pun1 SRS_Marsh_Dry.A.say1 SRS_Marsh_Dry.B.bel1 SRS_Marsh_Dry.C.uro1
TA 0.1131000 1.512350 4.220350 0.005350000 0.945900
SEA 0.2051407 1.478415 2.488708 0.009703827 1.170581
SEAc 0.4102814 1.971220 2.844238 0.019407653 1.755872
SRS_Marsh_Dry.CELSPP SRS_Marsh_Dry.E.eve1 SRS_Marsh_Dry.E.glo1 SRS_Marsh_Dry.E.suc1
TA 1.1697500 3.309900 4.386700 1.0058000
SEA 0.9171955 2.117159 2.946430 0.8508214
SEAc 1.1464944 2.419611 3.214287 1.0635267
SRS_Marsh_Dry.ERYSIM SRS_Marsh_Dry.F.chr1 SRS_Marsh_Dry.F.chr2 SRS_Marsh_Dry.F.con1 SRS_Marsh_Dry.FLOC
TA 0.7170500 7.443650 5.865900 2.420600 0.05415000
SEA 0.5810676 4.705859 3.499154 4.390483 0.09821724
SEAc 0.7263345 5.176445 3.936548 8.780965 0.19643447
SRS_Marsh_Dry.G.hol1 SRS_Marsh_Dry.G.hol2 SRS_Marsh_Dry.H.for2 SRS_Marsh_Dry.H.let1
TA 1.3163500 7.109050 4.175400 6.403100
SEA 0.7728014 2.053469 2.468690 1.929964
SEAc 0.8694016 2.129523 2.821359 1.972852
SRS_Marsh_Dry.H.lit1 SRS_Marsh_Dry.J.flo2 SRS_Marsh_Dry.L.goo1 SRS_Marsh_Dry.L.goo2
TA 2.987602 2.076750 6.142400 8.491900
SEA 2.807103 2.756889 2.450960 2.709415
SEAc 3.742804 4.135334 2.595134 2.802843
SRS_Marsh_Dry.L.mar1 SRS_Marsh_Dry.L.pun2 SRS_Marsh_Dry.N.flo1 SRS_Marsh_Dry.N.gyr1
TA 3.381400 2.042600 1.056950 0.0921500
SEA 2.295298 1.422723 1.341590 0.1671416
SEAc 2.754357 1.625969 2.012385 0.3342832
SRS_Marsh_Dry.N.vir1 SRS_Marsh_Dry.PALPAL SRS_Marsh_Dry.PELFEM SRS_Marsh_Dry.PERMAT
TA 1.177450 0.6223044 3.541200 1.016050
SEA 2.135658 0.4691840 1.166243 1.249359
SEAc 4.271316 0.5864800 1.243993 1.874039
SRS_Marsh_Dry.PLASCA SRS_Marsh_Dry.PROALL SRS_Marsh_Dry.R.gry1 SRS_Marsh_Dry.S.lac1
TA 4.199583 3.496600 4.896400 3.365200
SEA 7.617202 2.040133 2.884803 2.479092
SEAc 15.234404 2.331580 3.296918 2.892274
SRS_Marsh_Dry.T.mar1 SRS_Marsh_Wet.A.nat4 SRS_Marsh_Wet.BELSPP SRS_Marsh_Wet.C.bat1
TA 5.092800 0.3402000 2.856850 7.566250
SEA 5.754479 0.6170545 2.313007 3.510695
SEAc 7.672639 1.2341091 2.891259 3.861764
SRS_Marsh_Wet.C.und1 SRS_Marsh_Wet.C.uro1 SRS_Marsh_Wet.C.uro2 SRS_Marsh_Wet.CELSPP
TA 0.4213391 6.707000 3.245200 0.10000000
SEA 0.2304139 4.110290 2.570912 0.08774482
SEAc 0.2633302 4.697474 3.085094 0.11699310
SRS_Marsh_Wet.CHIRON SRS_Marsh_Wet.COENAG SRS_Marsh_Wet.COPEPOD SRS_Marsh_Wet.E.eve1
TA 0.05977673 0.800400 1.972819 0.9126500
SEA 0.10842300 0.731395 2.840617 0.6427273
SEAc 0.21684600 0.877674 4.260925 0.8034091
SRS_Marsh_Wet.E.glo1 SRS_Marsh_Wet.E.suc1 SRS_Marsh_Wet.F.chr1 SRS_Marsh_Wet.F.chr2 SRS_Marsh_Wet.FLOC
TA 1.732350 0.1670500 4.096150 1.5500500 0.549700
SEA 1.661254 0.2139610 1.581015 0.9484037 0.756087
SEAc 2.215005 0.3209414 1.686416 1.0537818 1.134131
SRS_Marsh_Wet.G.hol1 SRS_Marsh_Wet.G.hol2 SRS_Marsh_Wet.H.for1 SRS_Marsh_Wet.H.for2
TA 2.851017 3.1108630 1.2802500 2.176400
SEA 1.317086 0.8946906 0.6910471 2.345196
SEAc 1.386406 0.9188714 0.7897681 3.126929
SRS_Marsh_Wet.H.let1 SRS_Marsh_Wet.J.flo1 SRS_Marsh_Wet.J.flo2 SRS_Marsh_Wet.L.goo1
TA 10.356223 4.509250 4.764300 11.660924
SEA 2.537790 3.396242 4.643346 4.021360
SEAc 2.599687 4.245303 6.191129 4.204149
SRS_Marsh_Wet.L.goo2 SRS_Marsh_Wet.L.mac1 SRS_Marsh_Wet.L.mar1 SRS_Marsh_Wet.L.mic1
TA 19.235399 5.112400 4.570500 0.888000
SEA 5.296646 2.018331 3.735404 1.610654
SEAc 5.406993 2.137057 4.669256 3.221308
SRS_Marsh_Wet.L.mic2 SRS_Marsh_Wet.L.pun2 SRS_Marsh_Wet.LIBINC SRS_Marsh_Wet.M.sia1
TA 4.160450 2.714800 0.003450000 0.560600
SEA 3.748577 2.908464 0.006257608 1.016816
SEAc 4.998103 3.877952 0.012515216 2.033632
SRS_Marsh_Wet.MELTUB SRS_Marsh_Wet.O.aur1 SRS_Marsh_Wet.P.lat2 SRS_Marsh_Wet.PALPAL
TA 0.5128644 12.251350 0.7312000 3.156619
SEA 0.7363785 4.210441 0.5105810 1.533797
SEAc 1.1045678 4.432043 0.5956778 1.704219
SRS_Marsh_Wet.PELFEM SRS_Marsh_Wet.PERMAT SRS_Marsh_Wet.PROFAL SRS_Marsh_Wet.PROSPP SRS_NP_Dry.E.eve1
TA 4.364800 0.3824500 1.359300 0.4619000 5.439050
SEA 3.902606 0.6936876 1.359609 0.5596069 3.409424
SEAc 4.878258 1.3873751 1.812812 0.8394104 3.977662
SRS_NP_Dry.E.glo1 SRS_NP_Dry.F.chr1 SRS_NP_Dry.F.chr2 SRS_NP_Dry.FLOC SRS_NP_Dry.G.hol1
TA 1.936600 12.571200 0.2200500 0.1374000 1.965400
SEA 1.395043 5.097754 0.3991266 0.1694359 1.006367
SEAc 1.674052 5.522567 0.7982531 0.2541538 1.132163
SRS_NP_Dry.G.hol2 SRS_NP_Dry.H.for1 SRS_NP_Dry.H.for2 SRS_NP_Dry.L.goo1 SRS_NP_Dry.L.goo2
TA 8.257400 0.993150 0.7068000 2.740400 8.468100
SEA 2.845809 1.163801 0.6253408 1.407096 2.784315
SEAc 2.955263 1.745701 0.7816760 1.608109 2.930858
SRS_NP_Dry.L.mar1 SRS_NP_Dry.P.lat2 SRS_NP_Dry.PALPAL SRS_NP_Dry.PROFAL SRS_NP_Wet.BELSPP
TA 2.856000 2.755550 0.7968000 1.602550 2.029050
SEA 2.675335 1.984780 0.6497947 0.890335 2.097634
SEAc 3.344169 2.480975 0.8122433 1.038724 2.796845
SRS_NP_Wet.COENAG SRS_NP_Wet.E.eve1 SRS_NP_Wet.F.chr1 SRS_NP_Wet.FLOC SRS_NP_Wet.G.hol1
TA 0.7118000 4.047200 0.1405000 0.0870000 0.6977842
SEA 0.6559118 1.336816 0.2548388 0.1203651 0.6727677
SEAc 0.8745491 1.400474 0.5096776 0.1805477 0.8970236
SRS_NP_Wet.G.hol2 SRS_NP_Wet.H.for1 SRS_NP_Wet.H.for2 SRS_NP_Wet.H.let1 SRS_NP_Wet.L.goo2
TA 2.2348377 4.248750 0.5254000 0.8998385 1.262327
SEA 0.7054588 2.531095 0.3566264 1.6321265 1.568290
SEAc 0.7306537 2.952944 0.4279517 3.2642530 1.960362
SRS_NP_Wet.PALPAL SRS_NP_Wet.PELFEM SRS_NP_Wet.PROFAL SRS_NP_Wet.PROSPP SRS_NP_Wet.SAGLAN
TA 0.6040500 0.3571500 0.5504000 0.5607500 0.00785000
SEA 0.4954843 0.3062566 0.5169092 0.4143018 0.01423833
SEAc 0.6193554 0.3828207 0.6461365 0.5178773 0.02847665
SRS_Pond_Dry.A.mea1 SRS_Pond_Dry.A.nat1 SRS_Pond_Dry.A.nat2 SRS_Pond_Dry.A.nat3 SRS_Pond_Dry.A.nat4
TA 0.1414000 0.3139500 0.2915500 1.548250 1.5028500
SEA 0.2564712 0.3299708 0.2796233 1.306222 0.7998562
SEAc 0.5129425 0.4399611 0.3728311 1.632778 0.8998383
SRS_Pond_Dry.ALGBAL SRS_Pond_Dry.B.bel1 SRS_Pond_Dry.BELSPP SRS_Pond_Dry.BRAGRA SRS_Pond_Dry.C.uro1
TA 5.021450 0.3711500 0.296750 5.214650 5.358400
SEA 9.107903 0.4893939 0.538245 2.392782 2.045414
SEAc 18.215806 0.7340908 1.076490 2.658647 2.181774
SRS_Pond_Dry.CELSPP SRS_Pond_Dry.CLADOCERA SRS_Pond_Dry.COPEPOD SRS_Pond_Dry.E.eve1
TA 0.9440500 6.564355 30.122184 1.807800
SEA 0.7695930 6.603198 9.628609 1.575007
SEAc 0.9619913 8.253997 10.087114 2.100009
SRS_Pond_Dry.E.suc2 SRS_Pond_Dry.EPHEME SRS_Pond_Dry.F.chr1 SRS_Pond_Dry.F.con1 SRS_Pond_Dry.FLOC
TA 0.141250 0.02230364 15.353400 2.666100 4.245100
SEA 0.171250 0.04045433 5.560460 0.850934 2.528157
SEAc 0.256875 0.08090867 5.853116 0.907663 2.809064
SRS_Pond_Dry.G.hol1 SRS_Pond_Dry.G.hol2 SRS_Pond_Dry.H.for1 SRS_Pond_Dry.H.for2 SRS_Pond_Dry.H.lit1
TA 13.744800 15.038900 6.924750 0.5993000 3.719843
SEA 4.882678 4.101780 3.573536 0.3775427 1.908509
SEAc 5.126812 4.259541 3.970596 0.4530513 2.082010
SRS_Pond_Dry.J.flo1 SRS_Pond_Dry.J.flo2 SRS_Pond_Dry.L.goo1 SRS_Pond_Dry.L.goo2 SRS_Pond_Dry.L.gul1
TA 1.289250 2.843600 6.240900 13.193150 0.2894500
SEA 2.338441 1.410714 1.924557 3.604510 0.5250042
SEAc 4.676882 1.612245 2.001539 3.733242 1.0500085
SRS_Pond_Dry.L.gul2 SRS_Pond_Dry.L.gul3 SRS_Pond_Dry.L.gul4 SRS_Pond_Dry.L.mar1 SRS_Pond_Dry.L.mic1
TA 0.9750500 1.9928500 3.067500 3.763950 1.002300
SEA 0.4426663 0.7809741 2.041668 1.798821 1.397173
SEAc 0.4918515 0.8460553 2.333335 1.962350 2.095759
SRS_Pond_Dry.L.pla1 SRS_Pond_Dry.L.pun2 SRS_Pond_Dry.L.pun3 SRS_Pond_Dry.P.lat2 SRS_Pond_Dry.PALPAL
TA 6.637150 2.567500 0.01045000 5.772050 0.8540000
SEA 2.414475 1.788746 0.01895420 2.678368 0.7708134
SEAc 2.519452 2.235932 0.03790841 2.975965 0.9635167
SRS_Pond_Dry.PLASCA SRS_Pond_Dry.PROALL SRS_Pond_Dry.PROFAL SRS_Pond_Dry.R.gry2 SRS_Pond_Dry.S.lac1
TA 6.392476 1.989250 3.822750 2.165250 6.910400
SEA 3.495633 1.134402 1.429117 1.741326 4.791122
SEAc 3.995009 1.323469 1.524392 2.176657 5.749346
SRS_Pond_Wet.A.cal2 SRS_Pond_Wet.A.gry1 SRS_Pond_Wet.A.nat3 SRS_Pond_Wet.AMPHIPOD SRS_Pond_Wet.B.bel1
TA 2.335750 3.548650 0.02290000 8.867585 0.1585000
SEA 2.517088 5.156381 0.04153601 3.618512 0.2874872
SEAc 3.775632 7.734571 0.08307201 3.947467 0.5749744
SRS_Pond_Wet.BRAGRA SRS_Pond_Wet.C.bat1 SRS_Pond_Wet.C.uro1 SRS_Pond_Wet.C.uro2 SRS_Pond_Wet.CELSPP
TA 5.308600 0.0969500 1.502550 1.391450 1.7764500
SEA 3.020850 0.1758478 1.232649 2.523811 0.9889791
SEAc 3.398456 0.3516957 1.438090 5.047622 1.1538090
SRS_Pond_Wet.CLAJAM SRS_Pond_Wet.COENAG SRS_Pond_Wet.CORING SRS_Pond_Wet.E.glo1 SRS_Pond_Wet.E.suc1
TA 0.2730000 3.021700 1.040600 2.583500 1.387000
SEA 0.4951672 3.236603 1.887440 1.161046 1.335723
SEAc 0.9903345 4.315470 3.774879 1.250357 1.669654
SRS_Pond_Wet.E.suc2 SRS_Pond_Wet.ELECEL SRS_Pond_Wet.F.chr1 SRS_Pond_Wet.F.chr2 SRS_Pond_Wet.F.con1
TA 1.954400 0.582950 15.376500 3.310200 1.0390000
SEA 1.402705 1.057354 5.053229 2.244636 0.6202851
SEAc 1.683246 2.114709 5.240386 2.618742 0.7088972
SRS_Pond_Wet.FLOC SRS_Pond_Wet.H.for1 SRS_Pond_Wet.H.for2 SRS_Pond_Wet.H.let1 SRS_Pond_Wet.J.flo2
TA 7.563500 11.938300 0.763900 8.521198 1.049100
SEA 3.489992 7.292523 1.050163 2.728774 1.189635
SEAc 3.838991 8.204088 1.575244 2.858716 1.784453
SRS_Pond_Wet.L.goo1 SRS_Pond_Wet.L.goo2 SRS_Pond_Wet.L.gul2 SRS_Pond_Wet.L.gul3 SRS_Pond_Wet.L.gul4
TA 14.900307 26.276777 1.5192500 2.8108500 1.5454000
SEA 4.227608 4.637676 0.9022399 0.9184916 0.9048918
SEAc 4.304474 4.694233 1.0526132 0.9668332 1.0341620
SRS_Pond_Wet.L.mac1 SRS_Pond_Wet.L.mar1 SRS_Pond_Wet.L.mic1 SRS_Pond_Wet.L.mic2 SRS_Pond_Wet.L.pla1
TA 2.338800 8.400900 1.920600 1.385900 7.234850
SEA 1.635392 3.896903 1.635728 1.110035 3.242136
SEAc 1.907957 4.286594 1.908349 1.332043 3.432850
SRS_Pond_Wet.L.pla2 SRS_Pond_Wet.L.pun2 SRS_Pond_Wet.LIBINC SRS_Pond_Wet.M.sal1 SRS_Pond_Wet.M.sia1
TA 0.03805000 2.736450 1.013350 3.535000 8.583200
SEA 0.06901507 1.476591 1.473005 1.248363 2.598216
SEAc 0.13803013 1.722689 2.209508 1.326385 2.694446
SRS_Pond_Wet.N.pet1 SRS_Pond_Wet.N.vir1 SRS_Pond_Wet.O.aur1 SRS_Pond_Wet.P.lat1 SRS_Pond_Wet.P.lat2
TA 0.4744500 0.1568000 4.846000 5.903500 1.9931000
SEA 0.6081519 0.1643388 4.092165 4.299826 0.9848793
SEAc 0.8108692 0.2465083 5.115207 5.374782 1.1079893
SRS_Pond_Wet.PACLON SRS_Pond_Wet.PALPAL SRS_Pond_Wet.PELFEM SRS_Pond_Wet.PROALL SRS_Pond_Wet.PROFAL
TA 0.2107500 0.2831404 5.483400 1.5577500 1.296200
SEA 0.3822582 0.5135599 5.696812 0.6494697 1.859798
SEAc 0.7645164 1.0271198 7.595749 0.6927676 2.789697
SRS_Pond_Wet.PROSPP SRS_Pond_Wet.T.mar1 SRS_Sawgrass_Wet.L.goo2 TSL_Marsh_Dry.BLUE MITE
TA 0.01430000 0.6122500 2.425900 13.93749
SEA 0.02593733 0.5903674 2.707583 10.88686
SEAc 0.05187466 0.7871566 4.061374 12.24772
TSL_Marsh_Dry.CELSPP TSL_Marsh_Dry.CHIRON TSL_Marsh_Dry.COENAG TSL_Marsh_Dry.COPEPOD
TA 0.8188000 0.8404242 2.963500 4.158857
SEA 0.7579033 0.7293437 2.766415 3.017123
SEAc 0.9473791 0.8752125 3.688553 3.519977
TSL_Marsh_Dry.CORIXI TSL_Marsh_Dry.E.glo1 TSL_Marsh_Dry.EPHEME TSL_Marsh_Dry.ERYSIM
TA 0.613935 3.238300 1.169861 2.219350
SEA 0.798030 1.861644 1.460928 4.025456
SEAc 1.197045 2.094349 2.191392 8.050911
TSL_Marsh_Dry.F.chr1 TSL_Marsh_Dry.FLOC TSL_Marsh_Dry.G.hol2 TSL_Marsh_Dry.GERRID TSL_Marsh_Dry.H.for1
TA 2.187250 5.663900 0.04095000 1.895712 6.070750
SEA 2.541916 4.838688 0.07427508 2.310767 2.985627
SEAc 3.389221 6.048360 0.14855017 3.466151 3.257047
TSL_Marsh_Dry.H.for2 TSL_Marsh_Dry.L.goo1 TSL_Marsh_Dry.L.goo2 TSL_Marsh_Dry.PACLON
TA 6.515350 4.621300 5.458550 1.564700
SEA 3.327411 1.327904 2.013651 1.322094
SEAc 3.697123 1.388263 2.094197 1.652618
TSL_Marsh_Dry.PALPAL TSL_Marsh_Dry.PELFEM TSL_Marsh_Wet.BELSPP TSL_Marsh_Wet.BLUE MITE
TA 8.891000 11.143100 0.6617000 5.851769
SEA 3.989498 4.891456 0.5033108 6.889030
SEAc 4.352180 5.217553 0.6291385 8.037202
TSL_Marsh_Wet.BRAGRA TSL_Marsh_Wet.C.bim1 TSL_Marsh_Wet.C.man1 TSL_Marsh_Wet.C.uro1
TA 1.308300 11.038800 0.2037000 6.818950
SEA 1.581092 5.768252 0.3694709 3.259145
SEAc 2.108123 6.592288 0.7389419 3.585060
TSL_Marsh_Wet.CELSPP TSL_Marsh_Wet.CHIRON TSL_Marsh_Wet.COENAG TSL_Marsh_Wet.COLEOA
TA 0.10220000 1.711746 0.2021000 8.74170
SEA 0.07219271 2.398871 0.2885892 11.17291
SEAc 0.09024088 3.598306 0.4328838 16.75937
TSL_Marsh_Wet.COPEPOD TSL_Marsh_Wet.CORIXI TSL_Marsh_Wet.E.glo1 TSL_Marsh_Wet.EPHEME
TA 10.42444 1.261477 3.786850 1.119150
SEA 14.43978 2.288066 3.169209 1.580936
SEAc 19.25304 4.576133 3.803051 2.371403
TSL_Marsh_Wet.ERYSIM TSL_Marsh_Wet.F.chr1 TSL_Marsh_Wet.FLOC TSL_Marsh_Wet.G.hol1 TSL_Marsh_Wet.G.hol2
TA 7.317300 3.453250 6.718000 8.574067 7.795195
SEA 5.511569 1.415846 6.399855 3.502530 2.743539
SEAc 6.889462 1.533833 8.533140 3.661736 2.862823
TSL_Marsh_Wet.GERRID TSL_Marsh_Wet.H.for1 TSL_Marsh_Wet.H.for2 TSL_Marsh_Wet.L.goo1
TA 0.1638000 6.741550 2.739150 13.165500
SEA 0.2100045 3.425570 1.911412 5.887892
SEAc 0.3150067 3.711034 2.293694 6.155524
TSL_Marsh_Wet.L.goo2 TSL_Marsh_Wet.L.mar1 TSL_Marsh_Wet.N.pet1 TSL_Marsh_Wet.PALPAL
TA 8.266650 0.6286000 2.424900 1.1481500
SEA 3.464346 0.7005318 2.384884 0.8079172
SEAc 3.583807 1.0507977 3.179845 0.9695006
TSL_Marsh_Wet.PELFEM TSL_Marsh_Wet.PLASCA TSL_Marsh_Wet.UTRFOL TSL_NP_Dry.AMPHIPOD
TA 18.31255 0.7285917 5.240550 4.379633
SEA 18.21177 0.8579299 9.505306 3.694656
SEAc 22.76472 1.1439065 19.010613 4.310432
TSL_NP_Dry.BLUE MITE TSL_NP_Dry.CLADOCERA TSL_NP_Dry.COPEPOD TSL_NP_Wet.BLUE MITE TSL_NP_Wet.BRAGRA
TA 29.54951 0 0.01915806 6.071179 0.836500
SEA 23.44431 NaN 0.03474888 3.957201 1.187544
SEAc 26.79350 NaN 0.06949777 4.352921 1.781315
TSL_NP_Wet.C.bim1 TSL_NP_Wet.C.man1 TSL_NP_Wet.C.uro1 TSL_NP_Wet.CELSPP TSL_NP_Wet.CHIRON
TA 8.104500 4.929300 2.080600 0.2706000 0.002905156
SEA 4.655887 5.410425 1.649277 0.3261899 0.004251934
SEAc 5.173208 TSL_NP_Wet.CLADOCERA TSL_NP_Wet.COENAG TSL_NP_Wet.COLEOA TSL_NP_Wet.COPEPOD 1.105078 0.2117611
SEAc 4.951553 0.518928 0.1753931 1.243213 0.4235222
TSL_NP_Wet.E.eve1 TSL_NP_Wet.E.glo1 TSL_NP_Wet.ERYSIM TSL_NP_Wet.F.chr1 TSL_NP_Wet.FLOC
TA 0.1235500 1.958300 4.046350 0.1540000 1.936900
SEA 0.1812525 2.386832 3.826611 0.2793251 1.573545
SEAc 0.2718787 3.580248 4.783264 0.5586502 1.888254
TSL_NP_Wet.G.hol1 TSL_NP_Wet.G.hol2 TSL_NP_Wet.GERRID TSL_NP_Wet.H.for1 TSL_NP_Wet.H.for2
TA 1.0241939 1.818711 0.3117000 3.467500 3.186900
SEA 0.7359171 1.433096 0.4115832 1.534389 2.603541
SEAc 0.8831005 1.671946 0.6173748 1.687827 3.124249
TSL_NP_Wet.L.goo1 TSL_NP_Wet.L.goo2 TSL_NP_Wet.L.mar1 TSL_NP_Wet.PACLON TSL_NP_Wet.PALPAL
TA 2.526500 2.530450 3.847500 0.6805504 1.639143
SEA 1.976983 2.510608 3.597536 0.4948009 1.282476
SEAc 2.259409 3.138259 4.796714 0.5937611 1.603096
TSL_NP_Wet.PELFEM TSL_NP_Wet.PLASCA TSL_NP_Wet.PONCOR TSL_Pond_Dry.AMPHIPOD TSL_Pond_Dry.CHIRON
TA 4.374900 1.482504 0.01540000 4.546596 0.1144276
SEA 7.935191 2.688964 0.02793251 8.246614 0.2075487
SEAc 15.870382 5.377928 0.05586502 16.493227 0.4150973
TSL_Pond_Dry.COPEPOD TSL_Pond_Dry.FLOC TSL_Pond_Dry.G.hol1 TSL_Pond_Dry.G.hol2 TSL_Pond_Dry.H.for2
TA 4.118571 4.988250 16.611653 7.965550 0.1676500
SEA 3.816691 4.402786 3.771006 2.678898 0.2069176
SEAc 4.452806 5.870381 3.885279 2.778116 0.3103763
TSL_Pond_Dry.L.goo1 TSL_Pond_Dry.L.goo2 TSL_Pond_Dry.M.ber1 TSL_Pond_Dry.MELTUB TSL_Pond_Dry.N.pet1
TA 1.725100 10.297000 0.2308000 0.8681819 7.180700
SEA 2.232639 5.183537 0.3148177 1.5747078 3.132412
SEAc 3.348959 5.831479 0.4722266 3.1494156 3.281574
TSL_Pond_Dry.PALPAL TSL_Pond_Wet.AMPHIPOD TSL_Pond_Wet.BRAGRA TSL_Pond_Wet.C.uro1 TSL_Pond_Wet.C.uro2
TA 0.8237000 2.234734 0.6362000 0.1592500 1.508100
SEA 0.6297397 4.053360 0.7480896 0.2888475 1.096284
SEAc 0.7871747 8.106720 1.1221344 0.5776951 1.233319
TSL_Pond_Wet.COPEPOD TSL_Pond_Wet.FLOC TSL_Pond_Wet.G.hol1 TSL_Pond_Wet.G.hol2 TSL_Pond_Wet.H.for2
TA 0.4585087 5.220200 4.824072 18.396137 0.2938000
SEA 0.4235580 5.746515 2.132552 3.591808 0.3693081
SEAc 0.5647439 7.662020 2.310264 3.643119 0.5539621
TSL_Pond_Wet.L.goo1 TSL_Pond_Wet.L.goo2 TSL_Pond_Wet.L.mac1 TSL_Pond_Wet.L.sic1 TSL_Pond_Wet.M.alb2
TA 20.921078 17.826440 0.5407000 4.983750 0.0880000
SEA 7.301822 6.233150 0.4512536 1.863624 0.1596143
SEAc 7.593894 6.392974 0.5640670 1.935302 0.3192287
TSL_Pond_Wet.M.sal1 TSL_Pond_Wet.M.sia1 TSL_Pond_Wet.N.pet1 TSL_Pond_Wet.OSTRACOD TSL_Pond_Wet.P.lat2
TA 12.555000 9.395400 6.341300 0.3252554 0.1547000
SEA 3.111286 2.073522 2.902418 0.4009106 0.2805948
SEAc 3.211650 2.110549 3.109733 0.6013658 0.5611895
TSL_Pond_Wet.PALPAL TSL_Pond_Wet.T.mar1
TA 1.0741414 20.095900
SEA 0.7129872 9.136515
SEAc 0.8318184 9.897891#write.csv(t(global.iso.ML), file = "all_spp_ML_iso.csv")5 Post-Invasion SIBER model
#set up the model parameters
# options for running jags
parms <- list()
parms$n.iter <- 2 * 10^6 # number of iterations to run the model for
parms$n.burnin <- 1 * 10^4 # discard the first set of values
parms$n.thin <- 100 # thin the posterior by this many
parms$n.chains <- 2 # run this many chains
parms$save.output = T
parms$save.dir = getwd()
# define the priors
priors <- list()
priors$R <- 1 * diag(2)
priors$k <- 2
priors$tau.mu <- 1.0E-3
# fit the ellipses which uses an Inverse Wishart prior
# on the covariance matrix Sigma, and a vague normal prior on the
# means. Fitting is via the JAGS method.
global.iso.ellipses.posterior <- siberMVN(global.iso.siber, parms, priors)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
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Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
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Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
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Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
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Graph information:
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Graph information:
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Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
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Graph information:
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Unobserved stochastic nodes: 3
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 29
Unobserved stochastic nodes: 3
Total graph size: 44
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 47
Unobserved stochastic nodes: 3
Total graph size: 62
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
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|
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|************************************** | 76%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|********************** | 44%
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|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
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|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 31
Unobserved stochastic nodes: 3
Total graph size: 46
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|
|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
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|
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|
|****************************************** | 84%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|**************** | 32%
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|
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|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
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|****************************************** | 84%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|**************** | 32%
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|***************** | 34%
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|
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|
|********************** | 44%
|
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|************************ | 48%
|
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|
|************************** | 52%
|
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|
|**************************** | 56%
|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|
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|******************************************* | 86%
|
|******************************************** | 88%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 39
Unobserved stochastic nodes: 3
Total graph size: 54
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 43
Unobserved stochastic nodes: 3
Total graph size: 58
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
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|****** | 12%
|
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|******************************** | 64%
|
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|
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|
|************************************** | 76%
|
|*************************************** | 78%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 50
Unobserved stochastic nodes: 3
Total graph size: 65
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|**************** | 32%
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|
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|
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|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|
|**************** | 32%
|
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|
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|********************** | 44%
|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
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|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
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|************************************ | 72%
|
|************************************* | 74%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|
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|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 28
Unobserved stochastic nodes: 3
Total graph size: 43
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|** | 4%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
|
|* | 2%
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 30
Unobserved stochastic nodes: 3
Total graph size: 45
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|************************************ | 72%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
|
|********************************* | 66%
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|************************************ | 72%
|
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|************************************** | 76%
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|*************************************** | 78%
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|**************************************** | 80%
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|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|************************************ | 72%
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|************************************* | 74%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|******************************** | 64%
|
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|***** | 10%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|
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|
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|******************************** | 64%
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|
|**************** | 32%
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
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|
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
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|
|**************************************** | 80%
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 28
Unobserved stochastic nodes: 3
Total graph size: 43
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|************ | 24%
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|
|*************** | 30%
|
|**************** | 32%
|
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|****************** | 36%
|
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|
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|
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|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
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|
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|
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|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 30
Unobserved stochastic nodes: 3
Total graph size: 45
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
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|* | 2%
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|
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|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
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|****** | 12%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 29
Unobserved stochastic nodes: 3
Total graph size: 44
Initializing model
|
| | 0%
|
|* | 2%
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|
|***************** | 34%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 57
Unobserved stochastic nodes: 3
Total graph size: 72
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 84
Unobserved stochastic nodes: 3
Total graph size: 99
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|****** | 12%
|
|******* | 14%
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|********************************************** | 92%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|******************************** | 64%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 29
Unobserved stochastic nodes: 3
Total graph size: 44
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|
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|
|******************************** | 64%
|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|
|**************** | 32%
|
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|
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|
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|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
|********* | 18%
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|**************** | 32%
|
|***************** | 34%
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|
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|
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|
|********************** | 44%
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|*********************** | 46%
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|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
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|
|***************************** | 58%
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
|**************** | 32%
|
|***************** | 34%
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|
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|
|********************* | 42%
|
|********************** | 44%
|
|************************ | 48%
|
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|
|************************** | 52%
|
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|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
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|
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|************ | 24%
|
|************* | 26%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|************************************** | 76%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
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|********************************** | 68%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 31
Unobserved stochastic nodes: 3
Total graph size: 46
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
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|************************************** | 76%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********************** | 44%
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|
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|
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|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|**************** | 32%
|
|***************** | 34%
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|
|********************* | 42%
|
|********************** | 44%
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|
|************************ | 48%
|
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|
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|
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|
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|
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|****************************** | 60%
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|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|****************************************** | 84%
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|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
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|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
|************************* | 50%
|
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|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|***** | 10%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|************************************* | 74%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|
|************************** | 52%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
|* | 2%
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|** | 4%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
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|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
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|** | 4%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|************************************** | 76%
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
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|
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|****************************************** | 84%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 35
Unobserved stochastic nodes: 3
Total graph size: 50
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 29
Unobserved stochastic nodes: 3
Total graph size: 44
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|************************************ | 72%
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|*************************************** | 78%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|**************** | 32%
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|******************************** | 64%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|
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|******************************************** | 88%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 72
Unobserved stochastic nodes: 3
Total graph size: 87
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 41
Unobserved stochastic nodes: 3
Total graph size: 56
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 28
Unobserved stochastic nodes: 3
Total graph size: 43
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 33
Unobserved stochastic nodes: 3
Total graph size: 48
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|
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|******************************* | 62%
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|******************************** | 64%
|
|********************************* | 66%
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|************************************ | 72%
|
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|************************************** | 76%
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|*************************************** | 78%
|
|**************************************** | 80%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 58
Unobserved stochastic nodes: 3
Total graph size: 73
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|**************** | 32%
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|********************** | 44%
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|
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|******************************** | 64%
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|************************************ | 72%
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|************************************** | 76%
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|**************************************** | 80%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
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|
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|**************************************************| 100%#calculate posterior estimates of ellipses for all groups
SEA.B<-siberEllipses(global.iso.ellipses.posterior)
colnames(SEA.B)<-names(global.iso.ellipses.posterior)5.1 Extract Post-Invasion Output
# do similar to get the modes, taking care to pick up multimodal posterior
# distributions if present
SEA.B.modes<-lapply(
as.data.frame(SEA.B),
function(x,...){tmp<-hdrcde::hdr(x)$mode},
prob = cr.p, all.modes=T)
Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'# do similar to get the modes, taking care to pick up multimodal posterior
# distributions if present
SEA.B.modes<-lapply(
as.data.frame(SEA.B),
function(x,...){tmp<-hdrcde::hdr(x)$mode},
prob = cr.p, all.modes=T)Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'#need to do the same as for the credible intervals to get the colnames to be
#the names not the numbers
write.csv(SEA.B.modes,
file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv")
#read back in
SEA.B.modes.matrix <- read.csv(file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv",
header = T,
row.names = 1)
#change colnames
colnames(SEA.B.modes.matrix)<-colnames(global.iso.ML)
#transpose this matrix
SEA.B.modes.matrix.t<-t(SEA.B.modes.matrix)
#change the name of what is now a single column to mode
colnames(SEA.B.modes.matrix.t)<-"mode"
#write out the final csv over the previous mode csv
write.csv(SEA.B.modes.matrix.t,
file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv")5.2 Plots by slough, habitat, season
5.2.1 Slough by Habitat
post.plot.data <- SEA.B %>% as.data.frame() %>%
pivot_longer(everything(), names_to = "Slough_Habitat_Season_Species", values_to = "SEA_B") %>%
separate("Slough_Habitat_Season_Species", into = c("Slough_Habitat_Season", "Species"),
sep = "[//.]", extra = "merge") %>%
separate(Slough_Habitat_Season, into = c("Slough", "Habitat", "Season"), sep = "_")
#plot these data
windowsFonts(Times = windowsFont("Times New Roman"))
(post.seab<-ggplot(post.plot.data, aes(x = Species, y = SEA_B, fill = Season))+
geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.5)+
facet_wrap(~Slough*Habitat, scales = "free", ncol = 1)+
scale_fill_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+
#scale_y_continuous(limits = c(0, 10), expand = c(0.01, 0))+
coord_cartesian(ylim = c(0,10))+
theme_bw(base_size = 12)+
xlab(NULL)+
ylab(expression("Stable Isotope Niche Area - SEA"["b"]))+
theme(text = element_text(family = "Times", size = 12),
axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
#panel.border = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 8),
legend.key.size = unit(0.75, "line"))
)Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.
#ggsave(filename = "post_SEAb.tiff", plot = post.seab, dpi = 600, width = 300, height = 225, units = "mm")5.2.2 Seasonal comparisons
post.plot.data.seasonal.comparisons <- post.plot.data %>%
filter(
# filter out extraneous habitat levels!Habitat %in% c("Edge", "Sawgrass"),
#filter for SRS marsh species size classes with seasonal comparisons
Species %in% c(
"C.uro1",
"CELSPP",
"E.eve1",
"E.glo1",
"E.suc1",
"F.chr1",
"F.chr2",
"G.hol1",
"G.hol2",
"H.for2",
"H.let1",
"J.flo2",
"L.goo1",
"L.goo2",
"L.mar1",
"L.pun2",
"PELFEM"
) &
Slough == "SRS" & Habitat == "Marsh" |
#now for SRS near-pond species
Species %in% c(
"E.eve1",
"F.chr1",
"G.hol1",
"G.hol2",
"H.for1",
"H.for2",
"L.goo2",
"PALPAL",
"PROFAL"
Slough == "SRS" & Habitat == "NP" |
#now for SRS pond species
Species %in% c(
"A.nat3",
"B.bel1",
"BRAGRA",
"C.uro1",
"CELSPP",
"E.suc2",
"F.chr1",
"F.con1",
"H.for1",
"H.for2",
"J.flo2",
"L.goo1",
"L.goo2",
"L.gul2",
"L.gul3",
"L.gul4",
"L.mar1",
"L.mic1",
"L.pla1",
"L.pun2",
"L.pla1",
"L.pun2",
"P.lat2",
"PROALL",
"PROFALL"
) & Slough == "SRS" & Habitat == "Pond" |
#now do the same for TSL
Species %in% c(
"CELSPP",
"COENAG",
"CORIXI",
"E.glo1",
"EPHEME",
"ERYSIM",
"F.chr1",
"G.hol2",
"H.for1",
"H.for2",
"L.goo1",
"L.goo2",
"PALPAL"
) &
Slough == "TSL" & Habitat == "Marsh" |
#no NP dry season TSL data - skip NP
Species %in% c(
"AMPHIPOD",
"G.hol1",
"G.hol2",
"H.for2",
"L.goo1",
"L.goo2",
"N.pet1"
) & Slough == "TSL" & Habitat == "Pond"
) %>%
Species = case_when(
Species == "CELSPP" ~ "Celithemis",
Species == "COENAG" ~ "Coenagrionidae",
Species == "CORIXI" ~ "Corixidae",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERYSIM" ~ "Erythemis",
Species == "PALPAL" ~ "Palaemonetes paludosus",
Species == "PELFEM" ~ "Pelocoris femoratus",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BRAGRA" ~ "Brachymesia",
Species == "PROALL" ~ "Procambarus alleni",
T ~ Species
)
)
(post.seab.season.plot<-ggplot(post.plot.data.seasonal.comparisons %>%
filter(Habitat != "NP") %>%
mutate(Season = fct_rev(Season)),
aes(x = Species, y = SEA_B, fill = Season))+
geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.8)+
facet_grid(Slough ~ Habitat, scales = "free")+
scale_fill_viridis_d(aesthetics = "fill", begin = 0.2, end = 0.88)+
coord_cartesian(ylim = c(0, 10))+
theme_bw(base_size = 12)+
xlab(NULL)+
ylab(expression("Stomach Content Niche Area - SEA"["b"]))+
theme(text = element_text(family = "Times", size = 12),
axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 4),
#panel.border = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 8),
legend.key.size = unit(0.75, "line"))
)
# ggsave(filename = "post_SEAb_seasonal_comparisons.tiff", plot = post.seab.season.plot,
# dpi = 600, height = 150, width = 225, units = "mm")
5.3 Niche Ellipse Plots
For adults of the two most abundant fishes
(iso.niche.ellipse.plot <- ggplot(iso.merge %>%
filter(size_class %in% c("G.hol2", "L.goo2"),
Habitat != "Sawgrass",
!(Habitat == "NP" & Slough == "TSL")) %>%
mutate(Species = case_when(
size_class == "G.hol2" ~ "Eastern Mosquitofish (adults)",
size_class == "L.goo2" ~ "Bluefin Killifish (adults)"
)),
aes(x = d13C, y = d15N, color = Season, fill = Season))+
stat_conf_ellipse(geom = "polygon", alpha = 0.8)+
facet_grid(Species ~ Habitat*Slough, scales = "free")+
labs(y = expression(italic(delta)^15*N~("‰")), x = expression(italic(delta)^13*C~("‰")))+
scale_fill_viridis_d(end = 0.8)+
scale_color_viridis_d(end = 0.8)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
)
# ggsave(filename = "iso_niche_ellipse_plot.tiff", plot = iso.niche.ellipse.plot,
# height = 150, width = 200, units = "mm", dpi = 300)5.4 Post-Invasion Comparisons
Calculate pairwise comparisons
#turn output into a list
SEA.B.list <- as.list(as.data.frame(SEA.B))
#do all pairwise comparisons with function from tRophicPosition package
SEA.B.pairwise <- pairwiseComparisons(SEA.B.list, print = F)
colnames(SEA.B.pairwise) <- rownames(SEA.B.pairwise)###Seasonal Comparisons #### SRS marsh wet vs dry
srs.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
srs.marsh.wet.dry.direct.pairwise <- srs.marsh.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.marsh.wet.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.marsh.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(srs.marsh.wet.dry.direct.pairwise,
# file = "srs_marsh_wet_dry_SEAB_prob_diff.xlsx")5.4.0.1 SRS near pond wet vs dry
srs.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
srs.np.wet.dry.direct.pairwise <- srs.np.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.np.wet.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(srs.np.wet.dry.direct.pairwise,
# file = "srs_np_wet_dry_SEAB_prob_diff.xlsx")5.4.0.2 SRS pond wet vs dry
srs.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
srs.pond.wet.dry.direct.pairwise <- srs.pond.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.pond.wet.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "BRAGRA" ~ "B. gravida",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(srs.pond.wet.dry.direct.pairwise,
# file = "srs_pond_wet_dry_SEAB_prob_diff.xlsx")5.4.0.3 TSL marsh wet vs dry
tsl.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
tsl.marsh.wet.dry.direct.pairwise <- tsl.marsh.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.marsh.wet.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.marsh.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "BLUE MITE" ~ "Hydrachnidia",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CORIXI" ~ "Corixidae",
`Size Class` == "EPHEME" ~ "Ephemeroptera",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "GERRID" ~ "Gerridae",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(tsl.marsh.wet.dry.direct.pairwise,
# file = "tsl_marsh_wet_dry_SEAB_prob_diff.xlsx")5.4.0.4 TSL near pond wet vs dry
tsl.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
tsl.np.wet.dry.direct.pairwise <- tsl.np.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.np.wet.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "BLUE MITE" ~ "Hydrachnidia",
`Size Class` == "CLADOCERA" ~ "Cladocera",
`Size Class` == "COPEPOD" ~ "Copepoda",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(tsl.np.wet.dry.direct.pairwise,
# file = "tsl_np_wet_dry_SEAB_prob_diff.xlsx")5.4.0.5 TSL pond wet vs dry
tsl.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond") %>%
pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>%
filter(!is.na(Wet), !is.na(Dry))
tsl.pond.wet.dry.direct.pairwise <- tsl.pond.wet.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.pond.wet.dry.pairwise)) %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>%
rename(`Dry < Wet` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Dry < Wet` >= 0.95 ~ "Decrease",
`Dry < Wet` < 0.05 ~ "Increase",
`Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
`Percent Change` = ((Wet-Dry)/Dry)*100,
`Size Class` = case_when(
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "COPEPOD" ~ "Copepoda",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)
#write this out to excel
# write.xlsx(tsl.pond.wet.dry.direct.pairwise,
# file = "tsl_pond_wet_dry_SEAB_prob_diff.xlsx")5.4.0.6 Combine seasonal pairwise comparisons
seasonal.pairwise <- bind_rows(srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,
tsl.marsh.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)
write.csv(seasonal.pairwise,
"Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/season_pairiwise.csv")
#proportion of statistical changes in wet-dry comparisons
sum(seasonal.pairwise$`Dry < Wet`>=0.95 | seasonal.pairwise$`Dry < Wet`<=0.05)/nrow(seasonal.pairwise)
seasonal.pairwise %>%
filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#break it down per habitat
#marsh
#frequency
sum(srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 |
srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
nrow(bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise) %>%
filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#np
#frequency
sum(srs.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 |
srs.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
tsl.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
tsl.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
nrow(bind_rows(srs.np.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.np.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise) %>%
filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#pond
#frequency
sum(srs.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 |
srs.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
nrow(bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise) %>%
filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))5.4.0.7 Plot seasonal percent change
#Format data for plotting
all.season.direct.pairwise <- bind_rows(
srs.marsh.wet.dry.direct.pairwise,
srs.np.wet.dry.direct.pairwise,
srs.pond.wet.dry.direct.pairwise,
tsl.marsh.wet.dry.direct.pairwise,
tsl.pond.wet.dry.direct.pairwise #no tsl.np.wet.dry.direct.pairwise
) %>%
mutate(`Size Class` = case_when(`Size Class` == "C.uro1" ~ "M.uro1",
T ~ `Size Class`)) %>%
rename(`Wet < Dry` = `Dry < Wet`) %>%
mutate(
Trophic_Guild = case_when(
`Size Class` %in% c ("Ephemeroptera") ~ "Herbivore",
`Size Class` %in% c("P.lat2", "Amphipoda", "Chironomidae", "Copepoda") ~ "Detritivore",
`Size Class` %in% c(
"M.uro1",
"H.let1",
"G.hol1",
"G.hol2",
"E.suc1",
"E.suc2",
"J.flo2",
"P. alleni",
"P. fallax",
"P. paludosus"
) ~ "Omnivore",
`Size Class` %in% c(
"N.pet1",
"L.goo1",
"L.goo2",
"H.for1",
"H.for2",
"F.chr1",
"F.chr2",
"B. gravida",
"Celithimus spp.",
"Coenagrionidae",
"Corixidae",
"E. simplicicollis",
"E.eve1",
"E.glo1",
"Gerridae",
"Hydrachnidia",
"L.mar1",
"L.mic1",
"P. femoratus"
) ~ "Invertivore",
`Size Class` %in% c(
"L.pun2",
"L.pla1",
"L.gul4",
"L.gul3",
"L.gul2",
"A.nat3",
"B.bel1",
"F.con1"
) ~ "Mesopredator"
),
Trophic_Score = case_when(
Trophic_Guild == "Mesopredator" ~ 5,
Trophic_Guild == "Invertivore" ~ 4,
Trophic_Guild == "Omnivore" ~ 3,
Trophic_Guild == "Detritivore" ~ 2,
Trophic_Guild == "Herbivore" ~ 1
),
`Size Class` = fct_reorder(`Size Class`, Trophic_Score)
) %>%
rename(`Trophic Guild` = Trophic_Guild)
#Generate plots
windowsFonts(Times = windowsFont("Times New Roman"))
(season.percent.change.plot <- ggplot(data = all.season.direct.pairwise,
aes(y = `Size Class`, x = `Percent Change`, fill = Habitat,
color = Habitat, shape = Slough))+
geom_point(aes(size = `Wet < Dry`))+
scale_shape_manual(values = c(22,24))+
scale_fill_viridis_d(end = 0.8)+
scale_color_viridis_d(end = 0.8)+
scale_size_continuous(breaks = c(0.05, 0.25, 0.75, 0.95))+
facet_zoom(xlim = c(-100, 100))+
geom_hline(yintercept = 34.5, linetype = "dashed", color = viridis(n=4, option = "A")[1])+
geom_hline(yintercept = 15.5, linetype = "dashed", color = viridis(n=4, option = "A")[2])+
geom_hline(yintercept = 5.5, linetype = "dashed", color = viridis(n=4, option = "A")[3])+
geom_hline(yintercept = 1.5, linetype = "dashed", color = viridis(n=4, option = "A")[4])+
theme_bw()+
theme(text = element_text(family = "Times"),
axis.text.y = element_text(size = 6))
)
# ggsave("stable_isotopes_season_percent_change_plot.tiff",
# plot = season.percent.change.plot,
# dpi = 600, height = 250, width = 150, units = "mm")
#boxplot for percent change per habitat across all species
(season.percent.change.boxplot <- ggplot(data = all.season.direct.pairwise %>%
mutate(Habitat = gsub("NP", "Near Pond", Habitat)),
aes(y = `Percent Change`, x = Habitat))+
geom_boxplot()+
facet_zoom(ylim = c(-100, 100))+
scale_x_discrete(limits = c("Pond", "Near Pond", "Marsh"))+
theme_bw()+
theme(text = element_text(family = "Times"))
)
all.season.direct.pairwise.long <- all.season.direct.pairwise %>%
pivot_longer(cols = c(Wet, Dry), names_to = "Season", values_to = "Niche Area") %>%
mutate(Habitat = gsub("NP", "Near Pond", Habitat))
(season.niche.area.habitat.plot <- ggplot(data = all.season.direct.pairwise.long,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat)+
scale_x_discrete(limits = c("Wet", "Dry"))
#coord_cartesian(ylim = c(0,1))
)
season.niche.shifts <- all.season.direct.pairwise.long %>%
filter(`Wet < Dry` <= 0.05 | `Wet < Dry` >= 0.95)
season.niche.shifts %>% group_by(Habitat, Season) %>% tally()
(si.season.niche.area.shifts.habitat.plot <- ggplot(data = season.niche.shifts,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
#geom_violin(draw_quantiles = 0.5, alpha = 0.5)+
#facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat, scales = "free")+
scale_x_discrete(limits = c("Wet", "Dry"))+
#coord_cartesian(ylim = c(0,6))+
ylab(label = expression(Niche[SIA]))+
theme_bw()+
theme(text = element_text(family = "Times", size = 8),
axis.text = element_text(size = 6))
)
# ggsave("stable_isotope_seasonal_niche_shifts_per_habitat.tiff",
# plot = si.season.niche.area.shifts.habitat.plot,
# dpi = 600, height = 150, width = 250, units = "mm")
season.niche.shifts %>% group_by(Habitat, Season, `Trophic Guild`) %>% tally() %>%
arrange(Habitat, `Trophic Guild`) %>% print(n = 26)
(si.season.niche.area.shifts.guild.habitat.plot <- ggplot(data = season.niche.shifts,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
#geom_violin(draw_quantiles = 0.5, alpha = 0.5)+
#facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat*`Trophic Guild`, scales = "free_y")+
scale_x_discrete(limits = c("Wet", "Dry"))+
#coord_cartesian(ylim = c(0,6))+
ylab(label = expression(Niche[SIA]))+
theme_bw()+
theme(text = element_text(family = "Times", size = 8),
axis.text = element_text(size = 6))
)
# ggsave("stable_isotope_seasonal_niche_shifts_guild_per_habitat.tiff",
# plot = si.season.niche.area.shifts.guild.habitat.plot,
# dpi = 600, height = 150, width = 250, units = "mm")
###Slough Comparisons #### Marsh wet season
slough.marsh.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
slough.marsh.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Season == "Wet", Habitat == "Marsh") %>%
pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>%
filter(!is.na(SRS), !is.na(TSL))
slough.marsh.wet.direct.pairwise <- slough.marsh.wet.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.wet.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., slough.marsh.wet.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>%
rename(`TSL < SRS` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`TSL < SRS` >= 0.95 ~ "Decrease",
`TSL < SRS` < 0.05 ~ "Increase",
`TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
`Percent Change` = ((TSL-SRS)/SRS)*100,
`Size Class` = case_when(
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "COPEPOD" ~ "Copepoda",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)
#write this out to excel
#write.xlsx(slough.marsh.wet.direct.pairwise, file = "slough_marsh_wet_SEAB_prob_diff.xlsx")5.4.0.8 Near pond wet season
slough.np.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
slough.np.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Season == "Wet", Habitat == "NP") %>%
pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>%
filter(!is.na(SRS), !is.na(TSL))
slough.np.wet.direct.pairwise <- slough.np.wet.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(slough.np.wet.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., slough.np.wet.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>%
rename(`TSL < SRS` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`TSL < SRS` >= 0.95 ~ "Decrease",
`TSL < SRS` < 0.05 ~ "Increase",
`TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
`Percent Change` = ((TSL-SRS)/SRS)*100,
`Size Class` = case_when(
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "COPEPOD" ~ "Copepoda",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)
#write this out to excel
#write.xlsx(slough.np.wet.direct.pairwise, file = "slough_np_wet_SEAB_prob_diff.xlsx")5.4.0.9 Pond wet season
slough.pond.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
slough.pond.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Season == "Wet", Habitat == "Pond") %>%
pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>%
filter(!is.na(SRS), !is.na(TSL))
slough.pond.wet.direct.pairwise <- slough.pond.wet.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.wet.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., slough.pond.wet.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>%
rename(`TSL < SRS` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`TSL < SRS` >= 0.95 ~ "Decrease",
`TSL < SRS` < 0.05 ~ "Increase",
`TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
`Percent Change` = ((TSL-SRS)/SRS)*100,
`Size Class` = case_when(
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "COPEPOD" ~ "Copepoda",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)
#write this out to excel
#write.xlsx(slough.pond.wet.direct.pairwise, file = "slough_pond_wet_SEAB_prob_diff.xlsx")5.4.0.10 Marsh dry season
slough.marsh.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
slough.marsh.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Season == "Dry", Habitat == "Marsh") %>%
pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>%
filter(!is.na(SRS), !is.na(TSL))
slough.marsh.dry.direct.pairwise <- slough.marsh.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., slough.marsh.dry.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>%
rename(`TSL < SRS` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`TSL < SRS` >= 0.95 ~ "Decrease",
`TSL < SRS` < 0.05 ~ "Increase",
`TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
`Percent Change` = ((TSL-SRS)/SRS)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)
#write this out to excel
#write.xlsx(slough.marsh.dry.direct.pairwise, file = "slough_marsh_dry_SEAB_prob_diff.xlsx")5.4.0.11 Near pond dry season
No comparisons possible because of TSL near ponds drying early
5.4.0.12 Pond dry season
slough.pond.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
slough.pond.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Season == "Dry", Habitat == "Pond") %>%
pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>%
filter(!is.na(SRS), !is.na(TSL))
slough.pond.dry.direct.pairwise <- slough.pond.dry.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.dry.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., slough.pond.dry.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>%
rename(`TSL < SRS` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`TSL < SRS` >= 0.95 ~ "Decrease",
`TSL < SRS` < 0.05 ~ "Increase",
`TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
`Percent Change` = ((TSL-SRS)/SRS)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)
#write this out to excel
#write.xlsx(slough.pond.dry.direct.pairwise, file = "slough_pond_dry_SEAB_prob_diff.xlsx")5.4.0.13 Combine slough comparisons
slough.pairwise <-
bind_rows(
slough.marsh.dry.direct.pairwise,
slough.pond.dry.direct.pairwise,
slough.marsh.wet.direct.pairwise,
slough.np.wet.direct.pairwise,
slough.pond.wet.direct.pairwise
)
write.csv(slough.pairwise,
"Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/slough_pairwise.csv",
row.names = F)
#proportion of statistical changes in slough comparisons
sum(slough.pairwise$`TSL < SRS`>=0.95 | slough.pairwise$`TSL < SRS`<=0.05)/nrow(slough.pairwise)
slough.pairwise %>%
filter(`TSL < SRS` >= 0.95 | `TSL < SRS` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))5.4.1 Habitat Comparisons
5.4.1.1 SRS wet marsh vs near pond
srs.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "NP"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
srs.wet.marsh.np.direct.pairwise <- srs.wet.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.wet.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.wet.marsh.np.direct.pairwise, file = "srs_wet_marsh_np_SEAB_prob_diff.xlsx")5.4.1.2 SRS wet marsh vs pond
srs.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "Pond"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
srs.wet.marsh.pond.direct.pairwise <- srs.wet.marsh.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.wet.marsh.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Decrease",
`Marsh < Pond` < 0.05 ~ "Increase",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.wet.marsh.pond.direct.pairwise, file = "srs_wet_marsh_pond_SEAB_prob_diff.xlsx")5.4.1.3 SRS wet near pond vs pond
srs.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("NP", "Pond"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(NP), !is.na(Pond))
srs.wet.np.pond.direct.pairwise <- srs.wet.np.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.np.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.wet.np.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`NP < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`NP < Pond` >= 0.95 ~ "Decrease",
`NP < Pond` < 0.05 ~ "Increase",
`NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-NP)/NP)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.wet.np.pond.direct.pairwise, file = "srs_wet_np_pond_SEAB_prob_diff.xlsx")5.4.1.4 SRS dry marsh vs near pond
srs.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "NP"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
srs.dry.marsh.np.direct.pairwise <- srs.dry.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.dry.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.dry.marsh.np.direct.pairwise, file = "srs_dry_marsh_np_SEAB_prob_diff.xlsx")5.4.1.5 SRS dry marsh vs pond
srs.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "Pond"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
srs.dry.marsh.pond.direct.pairwise <- srs.dry.marsh.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.dry.marsh.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Decrease",
`Marsh < Pond` < 0.05 ~ "Increase",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PLASCA" ~ "Planorbella spp.",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.dry.marsh.pond.direct.pairwise, file = "srs_dry_marsh_pond_SEAB_prob_diff.xlsx")5.4.1.6 SRS dry near pond vs pond
srs.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("NP", "Pond"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(NP), !is.na(Pond))
srs.dry.np.pond.direct.pairwise <- srs.dry.np.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.np.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.dry.np.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`NP < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`NP < Pond` >= 0.95 ~ "Decrease",
`NP < Pond` < 0.05 ~ "Increase",
`NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-NP)/NP)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(srs.dry.np.pond.direct.pairwise, file = "srs_dry_np_pond_SEAB_prob_diff.xlsx")5.4.1.7 TSL wet marsh vs near pond
tsl.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("Marsh", "NP"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
tsl.wet.marsh.np.direct.pairwise <- tsl.wet.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.wet.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BLUE MITE" ~ "Hydrachnidia",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COLEOA" ~ "Coleoptera",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "GERRID" ~ "Gerridae",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PLASCA" ~ "Planorbella spp.",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.wet.marsh.np.direct.pairwise, file = "tsl_wet_marsh_np_SEAB_prob_diff.xlsx")5.4.1.8 TSL wet marsh vs pond
tsl.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("Marsh", "Pond"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
tsl.wet.marsh.pond.direct.pairwise <- tsl.wet.marsh.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.wet.marsh.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Decrease",
`Marsh < Pond` < 0.05 ~ "Increase",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.wet.marsh.pond.direct.pairwise, file = "tsl_wet_marsh_pond_SEAB_prob_diff.xlsx")5.4.1.9 TSL wet near pond vs pond
tsl.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("NP", "Pond"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(NP), !is.na(Pond))
tsl.wet.np.pond.direct.pairwise <- tsl.wet.np.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.np.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.wet.np.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`NP < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`NP < Pond` >= 0.95 ~ "Decrease",
`NP < Pond` < 0.05 ~ "Increase",
`NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-NP)/NP)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.wet.np.pond.direct.pairwise, file = "tsl_wet_np_pond_SEAB_prob_diff.xlsx")5.4.1.10 TSL dry marsh vs near pond
tsl.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("Marsh", "NP"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
tsl.dry.marsh.np.direct.pairwise <- tsl.dry.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.dry.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BLUE MITE" ~ "Hydrachnidia",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.dry.marsh.np.direct.pairwise, file = "tsl_dry_marsh_np_SEAB_prob_diff.xlsx")
5.4.1.11 TSL dry marsh vs pond
tsl.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("Marsh", "Pond"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
tsl.dry.marsh.pond.direct.pairwise <- tsl.dry.marsh.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.dry.marsh.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Decrease",
`Marsh < Pond` < 0.05 ~ "Increase",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PLASCA" ~ "Planorbella spp.",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.dry.marsh.pond.direct.pairwise, file = "tsl_dry_marsh_pond_SEAB_prob_diff.xlsx")5.4.1.12 TSL dry near pond vs pond
tsl.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
tsl.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "TSL", Habitat %in% c("NP", "Pond"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(NP), !is.na(Pond))
tsl.dry.np.pond.direct.pairwise <- tsl.dry.np.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.np.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., tsl.dry.np.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`NP < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`NP < Pond` >= 0.95 ~ "Decrease",
`NP < Pond` < 0.05 ~ "Increase",
`NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-NP)/NP)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
#write this out to excel
#write.xlsx(tsl.dry.np.pond.direct.pairwise, file = "tsl_dry_np_pond_SEAB_prob_diff.xlsx")5.4.1.13 Combine habitat comparisons
#Marsh-NP
marsh.np.comparisons <- bind_rows(srs.wet.marsh.np.direct.pairwise, srs.dry.marsh.np.direct.pairwise,
tsl.wet.marsh.np.direct.pairwise, tsl.dry.marsh.np.direct.pairwise)
#proportion of statistical changes in marsh-np comparisons
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)/nrow(marsh.np.comparisons)
#magnitude
marsh.np.comparisons %>%
filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#Marsh-Pond
marsh.pond.comparisons <- bind_rows(srs.wet.marsh.pond.direct.pairwise, srs.dry.marsh.pond.direct.pairwise,
tsl.wet.marsh.pond.direct.pairwise, tsl.dry.marsh.pond.direct.pairwise)
#proportion of statistical changes in marsh-pond comparisons
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)/nrow(marsh.pond.comparisons)
#magnitude
marsh.pond.comparisons %>%
filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#NP-Pond
np.pond.comparisons <- bind_rows(srs.wet.np.pond.direct.pairwise, srs.dry.np.pond.direct.pairwise,
tsl.wet.np.pond.direct.pairwise, tsl.dry.np.pond.direct.pairwise)
#proportion of statistical changes in marsh-np comparisons
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)/nrow(np.pond.comparisons)
#magnitude
np.pond.comparisons %>%
filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#combine into a list and export to Excel
habitat.comparisons <- list("Marsh-NP" = marsh.np.comparisons, "Marsh-Pond" = marsh.pond.comparisons,
"NP-Pond" = np.pond.comparisons)
write.xlsx(habitat.comparisons,
"Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/habitat_comparisons.xlsx")
#proportion of all habitat comparisons that were statistically different
(
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)+
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)+
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)
)/(
nrow(marsh.np.comparisons)+nrow(marsh.pond.comparisons)+nrow(np.pond.comparisons)
)
#magnitude
bind_rows(
marsh.np.comparisons %>% rename(Comparisons = `Marsh < NP`),
marsh.pond.comparisons %>% rename(Comparisons = `Marsh < Pond`),
np.pond.comparisons %>% rename(Comparisons = `NP < Pond`)
) %>% filter(Comparisons >= 0.95 | Comparisons <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))6 Pre- vs Post-Invasion SIBER
6.1 Load data
pre.post.invasion.isotopes <- read.csv("Outputs/Stable Isotopes/pre_post_invasion_isotopes.csv")6.2 Format data for SIBER
#format data for SIBER
pre.post.siber.data <- pre.post.invasion.isotopes %>% select(-c(Sample_Type)) %>%
rename(group = Species, iso1 = d13C, iso2 = d15N, community = Community) %>%
select(iso1, iso2, group, community)
#createSiberObject
pre.post.siber<-createSiberObject(pre.post.siber.data)
#write out samples sizes table
write.csv(pre.post.siber[["sample.sizes"]] %>% t(), file = "Outputs/Stable Isotopes/siber_pre_post_iso_n_table.csv")
# Create lists of plotting arguments to be passed onwards to each
# of the three plotting functions.
community.hulls.args <- list(col = 1, lty = 1, lwd = 1)
group.ellipses.args <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)
group.hull.args <- list(lty = 2, col = "grey20")
par(mfrow=c(1,1))
plotSiberObject(pre.post.siber,
ax.pad = 2,
hulls = F, community.hulls.args,
ellipses = T, group.ellipses.args,
group.hulls = T, group.hull.args,
bty = "L",
iso.order = c(1,2),
xlab = expression(paste(delta^{13}, "C (\u2030)")),
ylab = expression(paste(delta^{15}, "N (\u2030)"))
)6.3 Calculate TA, SEA, SEAc
#PALPAL post-invasion have some d13C values that seem like they're incorrect
pre.post.ML <- groupMetricsML(pre.post.siber)
print(pre.post.ML)
#write.csv(t(pre.post.ML), file = "pre_post_ML.csv")6.4 Run SIBER model
#use the same model parameters
# fit the ellipses which uses an Inverse Wishart prior
# on the covariance matrix Sigma, and a vague normal prior on the
# means. Fitting is via the JAGS method.
pre.post.ellipses.posterior <- siberMVN(pre.post.siber, parms, priors)
#calculate posterior estimates of ellipses for all groups
pre.post.SEA.B<-siberEllipses(pre.post.ellipses.posterior)
colnames(pre.post.SEA.B)<-names(pre.post.ellipses.posterior)6.4.1 Extract Output
#calculate credibility intervals
cr.p<-c(0.5, 0.95, 0.99) #vector of desired quantiles
#call hdrcde_hdr using lapply()
pre.post.SEA.B.credibles<-lapply(
as.data.frame(pre.post.SEA.B),
function(x,...){tmp<-hdrcde::hdr(x)$hdr},
prob = cr.p)
#match up group names
names(pre.post.SEA.B.credibles)<-names(pre.post.ellipses.posterior)
#this carries through the row names from matrices in SEA.B.credibles
#they don't turn out all that clean though, so adding a column later with the same info
pre.post.y <- do.call(function(...) {
tmp <- plyr::rbind.fill.matrix(...)
rownames(tmp) <- sapply(pre.post.SEA.B.credibles, function(i) {
rownames(i)
})
return(tmp)
}, pre.post.SEA.B.credibles)
pre.post.y
#object with cleaned-up row names
pre.post.interval<-rep(c("99%", "95%", "50%"), 32)
#replicated each name three times in sequence to match up with new data frame
pre.post.seab.names<-data.frame(rep(names(pre.post.SEA.B.credibles), each = 3))
colnames(pre.post.seab.names)<-"names"
#seab.names<-separate(seab.names, col = names, into = c('hab.season', 'spp'), extra = "merge")
#combine names with new data frame
pre.post.seab.cred.matrix<-data.frame(pre.post.seab.names, pre.post.interval, pre.post.y)
colnames(pre.post.seab.cred.matrix)<-c(colnames(pre.post.seab.names), "interval", "lower", "upper")
#write out to a csv
write.csv(pre.post.seab.cred.matrix,
file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_credibles.csv",
row.names = F)
# do similar to get the modes, taking care to pick up multimodal posterior
# distributions if present
pre.post.SEA.B.modes<-lapply(
as.data.frame(pre.post.SEA.B),
function(x,...){tmp<-hdrcde::hdr(x)$mode},
prob = cr.p, all.modes=T)
#need to do the same as for the credible intervals to get the colnames to be
#the names not the numbers
write.csv(pre.post.SEA.B.modes, file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")
#read back in
pre.post.SEA.B.modes.matrix<-read.csv(
file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv",
header = T, row.names = 1)
#change colnames
colnames(pre.post.SEA.B.modes.matrix)<-names(pre.post.ellipses.posterior)
#transpose this matrix
pre.post.SEA.B.modes.matrix.t<-t(pre.post.SEA.B.modes.matrix) %>% as.data.frame()
#change the name of what is now a single column to mode
colnames(pre.post.SEA.B.modes.matrix.t)<-"Mode"
#write out the final csv over the previous mode csv
write.csv(pre.post.SEA.B.modes.matrix.t,
file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")
# #test convergence for all ellipses
# pre.post.all.files <- dir(parms$save.dir, full.names = T)
#
# #find which onces are jags model files
# pre.post.model.files <- pre.post.all.files[grep("jags_output", pre.post.all.files)]
#
# #test convergence
# do.this <- 1 #this number indicates which file, in order, is being looked at
# load(pre.post.model.files[do.this])
#
# gelman.diag(output, multivariate = F)
# gelman.plot(output, auto.layout = F)6.4.2 Pre- vs Post-Invasion Comparisons
Calculate pairwise comparisons
#turn the output into a list
pre.post.SEA.B.list <- as.list(as.data.frame(pre.post.SEA.B))
#use the pairwiseComparisons function from tRophicPosition package
pre.post.pairwise <- pairwiseComparisons(pre.post.SEA.B.list, print = F)
colnames(pre.post.pairwise) <- rownames(pre.post.pairwise)Format data to have only pre- vs post-invasion comparisons
#reformat data to have only pre-invasion and post-invasion comparisons
pre.post.direct.pairwise <-
pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(
Invasion_Status_Spp,
into = c("Invasion_Status", "Species"),
sep = "[//.]"
) %>%
arrange(Species) %>%
unite("Invasion_Status_Spp", c(Invasion_Status, Species)) %>%
column_to_rownames(var = "Invasion_Status_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(
Invasion_Status_Spp,
into = c("Invasion_Status", "Species"),
sep = "[//.]"
) %>%
arrange(Species) %>%
unite("Invasion_Status_Spp", c(Invasion_Status, Species)) %>%
column_to_rownames(var = "Invasion_Status_Spp") %>%
t() %>% as.data.frame()
#format modes to bind with pairwise probs
pre.post.modes.wide <- pre.post.SEA.B.modes.matrix.t %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
separate(
Invasion_Status_Spp,
into = c("Invasion_Status", "Species"),
sep = "[//.]"
) %>%
pivot_wider(id_cols = Species,
names_from = Invasion_Status,
values_from = Mode) %>%
filter(!Species %in% c("Green Algae", "FLOC"))
pre.post.direct.spp.pairwise <-
pre.post.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.post.direct.pairwise)) %>%
separate(Species,
into = c("Invasion_Status", "Species"),
sep = "_") %>%
filter(!Species %in% c("Green Algae", "FLOC")) %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, contains("Invasion"), everything()) %>% select(-c(Invasion_Status)) %>%
rename(`Pre < Post` = ".") %>%
mutate(
#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Pre < Post` >= 0.95 ~ "Decrease",
`Pre < Post` < 0.05 ~ "Increase",
`Pre < Post` > 0.05 | `Pre < Post` < 0.95 ~ "No Change"
),
`Percent Change` = ((`Post-Invasion` - `Pre-Invasion`) / `Pre-Invasion`) *
100,
Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "ANISOP" ~ "Anisoptera",
Species == "BELSPP" ~ "Belostoma spp.",
Species == "CICURO" ~ "C. urophthalmus",
Species == "ERISUC" ~ "E. sucetta",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "HETFOR" ~ "H. formosa",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "PALPAL" ~ "P. paludosus",
Species == "POELAT" ~ "P. latipinna",
Species == "PROFAL" ~ "P. fallax"
)
) %>%
mutate(across(where(is.numeric), round, digits = 2))
#write this out to excel
write.csv(pre.post.direct.spp.pairwise,
file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_prob_diff.csv")
#proportion of statistical changes in pre-post comparisons
sum(
pre.post.direct.spp.pairwise$`Pre < Post` >= 0.95 |
pre.post.direct.spp.pairwise$`Pre < Post` <= 0.05
) / nrow(pre.post.direct.spp.pairwise)
6.4.3 Plot pre- vs post-invasion shifts
#reformat data to be able to make boxplots or violoin plots
pre.post.plot.data <- pre.post.SEA.B %>% as.data.frame() %>% pivot_longer(everything(), names_to = c("Invasion Status", "Species"),
names_sep = "[//.]", values_to = "SEA_B") %>%
filter(!Species %in% c("Green Algae", "FLOC", "PROFAL")) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "ANISOP" ~ "Anisoptera",
Species == "BELSPP" ~ "Belostoma spp.",
Species == "CICURO" ~ "C. urophthalmus",
Species =="ERISUC" ~ "E. sucetta",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "HETFOR" ~ "H. formosa",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "PALPAL" ~ "P. paludosus",
Species == "POELAT" ~ "P. latipinna"))
#plot these data
windowsFonts(Times = windowsFont("Times New Roman"))
(pre.post.seab<-ggplot(pre.post.plot.data %>%
mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(`Invasion Status`))),
aes(x = Species, y = SEA_B, fill = `Invasion Status`))+
#geom_violin()+
geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1)+
#facet_wrap(~Species)+
scale_color_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+
scale_y_continuous(limits = c(0, 22))+
theme_bw(base_size = 8)+
xlab(NULL)+
ylab("Niche Area")+
annotate("text", x = 4, y = 3.75, label = "*", size = 2)+
annotate("text", x = 5, y = 8.5, label = "*", size = 2)+
annotate("text", x = 6, y = 8.25, label = "*", size = 2)+
annotate("text", x = 7, y = 21, label = "*", size = 2)+
annotate("text", x = 9, y = 1.75, label = "*", size = 2)+
annotate("text", x = 10, y = 3, label = "*", size = 2)+
annotate("text", x = 13, y = 11, label = "*", size = 2)+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 4),
legend.key.size = unit(0.75, "line"))
)
#ggsave(filename = "pre_post_SEAb.tiff", plot = pre.post.seab, dpi = 600, width = 84, height = 60, units = "mm")---
title: "07_niches_SIBER_stable_isotopes"
author: "Peter Flood"
date: "2024-03-04"
output: 
  html_document:
    toc: true
    df_print: paged
    number_sections: true
  html_notebook:
    toc: true
    toc_float: true
    number_sections: true
---

Copy over from dpe_iso_niche.R

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(tidyverse) #data manipulation and plotting via ggplot
library(SIBER) #modeling isotopic niches
library(openxlsx) #importing and exporting excel files
library(coda) #Bayesian model diagnostics
library(tRophicPosition) #using their pairwise comparisons functions 
library(ggpubr) #plotting, arranging multiple ggplots 
library(ggforce) #facet_zoom function
library(viridis) #color palettes 
```

# Load data
```{r}
iso.merge<-read.csv(file = "Data/Stable Isotopes/isotope_merge_all.csv", header = T)
```

# Format data for SIBER
```{r}
#prepare data for SIBER
#need to unite the habitat and season columns into a single column called "community"
iso.slough.hab.season<-unite(iso.merge, col = "community", Slough, Habitat, Season, sep ="_") %>% 
  mutate(size_class = case_when(
    is.na(size_class) ~ Species,
    !is.na(size_class) ~ size_class
  ))

#isolate only the data needed for SIBER
iso.siber<-select(iso.slough.hab.season, d13C, d15N, size_class, community)
colnames(iso.siber)<-c("iso1", "iso2", "group", "community")

#dropping consumers where n < 3 or sd of either isotope is 0
siber.data<-iso.siber %>%
  group_by(group, community) %>%
  filter(n()>2, sd(iso1)!=0, sd(iso2)!=0, !is.na(iso1), !is.na(iso2)) %>% #remove groups with n < 3 and those that didn't get C or N peaks
  ungroup() %>%
  as.data.frame() %>% #SIBER needs this step to work 
  arrange(community, group) #this step makes it so colnames() of ML output are the same order as ellipse model output
#check what was removed
removed.consumers<-iso.siber[!(iso.siber$group %in% siber.data$group),]

#createSiberObject
global.iso.siber<-createSiberObject(siber.data)

#write out samples sizes table
#write.csv(global.iso.siber[["sample.sizes"]] %>% t(), file = "siber_global_iso_n_table.csv")

iso.sample.size <- global.iso.siber[["sample.sizes"]] %>% t() %>% 
  as.data.frame() %>% pivot_longer(cols = c(SRS_Edge_Dry:TSL_Pond_Wet), 
                                   values_to = "n", names_to = "Slough_Habitat_Season") %>% 
  filter(!is.na(n))

mean(iso.sample.size$n)
quantile(iso.sample.size$n, c(0.025, 0.5, 0.975))
max(iso.sample.size$n)


# Create lists of plotting arguments to be passed onwards to each 
# of the three plotting functions.
community.hulls.args <- list(col = 1, lty = 1, lwd = 1)
group.ellipses.args  <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)
group.hull.args      <- list(lty = 2, col = "grey20")

par(mfrow=c(1,1))
plotSiberObject(global.iso.siber,
                ax.pad = 2, 
                hulls = F, community.hulls.args, 
                ellipses = T, group.ellipses.args,
                group.hulls = T, group.hull.args,
                bty = "L",
                iso.order = c(1,2),
                xlab = expression(d13C),
                ylab = expression(d15N)
)
```

# Calculate TA, SEA, SEAc
```{r}
global.iso.ML <- groupMetricsML(global.iso.siber)
print(global.iso.ML)
#write.csv(t(global.iso.ML), file = "all_spp_ML_iso.csv")
```

# Post-Invasion SIBER model
```{r}
#set up the model parameters 
# options for running jags
parms <- list()
parms$n.iter <- 2 * 10^6   # number of iterations to run the model for
parms$n.burnin <- 1 * 10^4 # discard the first set of values
parms$n.thin <- 100     # thin the posterior by this many
parms$n.chains <- 2        # run this many chains
parms$save.output = T
parms$save.dir = getwd()

# define the priors
priors <- list()
priors$R <- 1 * diag(2)
priors$k <- 2
priors$tau.mu <- 1.0E-3

# fit the ellipses which uses an Inverse Wishart prior
# on the covariance matrix Sigma, and a vague normal prior on the 
# means. Fitting is via the JAGS method.
global.iso.ellipses.posterior <- siberMVN(global.iso.siber, parms, priors)

#calculate posterior estimates of ellipses for all groups
SEA.B<-siberEllipses(global.iso.ellipses.posterior)
colnames(SEA.B)<-names(global.iso.ellipses.posterior)
```

## Extract Post-Invasion Output
```{r}
#calculate credibility intervals
cr.p<-c(0.5, 0.95, 0.99) #vector of desired quantiles

#call hdrcde_hdr using lapply()
SEA.B.credibles<-lapply(
  as.data.frame(SEA.B), 
  function(x,...){tmp<-hdrcde::hdr(x)$hdr},
  prob = cr.p)

#match up group names
names(SEA.B.credibles)<-colnames(global.iso.ML)

#this carries through the row names from matrices in SEA.B.credibles
#they don't turn out all that clean though, so adding a column later with the same info
y <- do.call(function(...) {
  tmp <- plyr::rbind.fill.matrix(...)
  rownames(tmp) <- sapply(SEA.B.credibles, function(i) {
    rownames(i)
  })
  return(tmp)
}, SEA.B.credibles)

y

#object with cleaned-up row names
interval<-rep(c("99%", "95%", "50%"), 321)

#replicated each name three times in sequence to match up with new data frame
seab.names<-data.frame(rep(names(SEA.B.credibles), each = 3))
colnames(seab.names)<-"names"

#seab.names<-separate(seab.names, col = names, into = c('hab.season', 'spp'), extra = "merge")

#combine names with new data frame
seab.cred.matrix<-data.frame(seab.names, interval, y)
colnames(seab.cred.matrix)<-c(colnames(seab.names), "interval", "lower", "upper")

#write out to a csv
write.csv(seab.cred.matrix, 
          file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.credibles.csv")

# do similar to get the modes, taking care to pick up multimodal posterior
# distributions if present
SEA.B.modes<-lapply(
  as.data.frame(SEA.B), 
  function(x,...){tmp<-hdrcde::hdr(x)$mode},
  prob = cr.p, all.modes=T)

#need to do the same as for the credible intervals to get the colnames to be
#the names not the numbers
write.csv(SEA.B.modes, 
          file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv")

#read back in
SEA.B.modes.matrix <- read.csv(file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv",
                               header = T,
                               row.names = 1)

#change colnames
colnames(SEA.B.modes.matrix)<-colnames(global.iso.ML)
#transpose this matrix
SEA.B.modes.matrix.t<-t(SEA.B.modes.matrix)
#change the name of what is now a single column to mode
colnames(SEA.B.modes.matrix.t)<-"mode"
#write out the final csv over the previous mode csv
write.csv(SEA.B.modes.matrix.t, 
          file = "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/global.iso.SEA.B.modes.csv")
```

## Plots by slough, habitat, season
### Slough by Habitat
```{r}
post.plot.data <- SEA.B %>% as.data.frame() %>% 
  pivot_longer(everything(), names_to = "Slough_Habitat_Season_Species", values_to = "SEA_B") %>% 
  separate("Slough_Habitat_Season_Species", into = c("Slough_Habitat_Season", "Species"), 
           sep = "[//.]", extra = "merge") %>% 
  separate(Slough_Habitat_Season, into = c("Slough", "Habitat", "Season"), sep = "_") 

#plot these data
windowsFonts(Times = windowsFont("Times New Roman"))

(post.seab<-ggplot(post.plot.data, aes(x = Species, y = SEA_B, fill = Season))+
    geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.5)+
    facet_wrap(~Slough*Habitat, scales = "free", ncol = 1)+
    scale_fill_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+
    #scale_y_continuous(limits = c(0, 10), expand = c(0.01, 0))+
    coord_cartesian(ylim = c(0,10))+
    theme_bw(base_size = 12)+
    xlab(NULL)+
    ylab(expression("Stable Isotope Niche Area - SEA"["b"]))+
    theme(text = element_text(family = "Times", size = 12),
          axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
          #panel.border = element_blank(), 
          #panel.grid.major = element_blank(),
          #panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"),
          axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
          axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_blank(), legend.text = element_text(size = 8),
          legend.key.size = unit(0.75, "line"))
  
)

#ggsave(filename = "post_SEAb.tiff", plot = post.seab, dpi = 600, width = 300, height = 225, units = "mm")
```

### Seasonal comparisons
```{r}
post.plot.data.seasonal.comparisons <- post.plot.data %>%
  filter(
    # filter out extraneous habitat levels!Habitat %in% c("Edge", "Sawgrass"),
    #filter for SRS marsh species size classes with seasonal comparisons
    Species %in% c(
      "C.uro1",
      "CELSPP",
      "E.eve1",
      "E.glo1",
      "E.suc1",
      "F.chr1",
      "F.chr2",
      "G.hol1",
      "G.hol2",
      "H.for2",
      "H.let1",
      "J.flo2",
      "L.goo1",
      "L.goo2",
      "L.mar1",
      "L.pun2",
      "PELFEM"
    ) &
      Slough == "SRS" & Habitat == "Marsh" |
      #now for SRS near-pond species
      Species %in% c(
        "E.eve1",
        "F.chr1",
        "G.hol1",
        "G.hol2",
        "H.for1",
        "H.for2",
        "L.goo2",
        "PALPAL",
        "PROFAL"
      ) &
      Slough == "SRS" & Habitat == "NP" |
      #now for SRS pond species
      Species %in% c(
        "A.nat3",
        "B.bel1",
        "BRAGRA",
        "C.uro1",
        "CELSPP",
        "E.suc2",
        "F.chr1",
        "F.con1",
        "H.for1",
        "H.for2",
        "J.flo2",
        "L.goo1",
        "L.goo2",
        "L.gul2",
        "L.gul3",
        "L.gul4",
        "L.mar1",
        "L.mic1",
        "L.pla1",
        "L.pun2",
        "L.pla1",
        "L.pun2",
        "P.lat2",
        "PROALL",
        "PROFALL"
      ) & Slough == "SRS" & Habitat == "Pond" |
      #now do the same for TSL
      Species %in% c(
        "CELSPP",
        "COENAG",
        "CORIXI",
        "E.glo1",
        "EPHEME",
        "ERYSIM",
        "F.chr1",
        "G.hol2",
        "H.for1",
        "H.for2",
        "L.goo1",
        "L.goo2",
        "PALPAL"
      ) &
      Slough == "TSL" & Habitat == "Marsh" |
      #no NP dry season TSL data - skip NP
      Species %in% c(
        "AMPHIPOD",
        "G.hol1",
        "G.hol2",
        "H.for2",
        "L.goo1",
        "L.goo2",
        "N.pet1"
      ) & Slough == "TSL" & Habitat == "Pond"
  ) %>%
  mutate(
    Species = case_when(
      Species == "CELSPP" ~ "Celithemis",
      Species == "COENAG" ~ "Coenagrionidae",
      Species == "CORIXI" ~ "Corixidae",
      Species == "EPHEME" ~ "Ephemeroptera",
      Species == "ERYSIM" ~ "Erythemis",
      Species == "PALPAL" ~ "Palaemonetes paludosus",
      Species == "PELFEM" ~ "Pelocoris femoratus",
      Species == "AMPHIPOD" ~ "Amphipoda",
      Species == "BRAGRA" ~ "Brachymesia",
      Species == "PROALL" ~ "Procambarus alleni",
      T ~ Species
    )
  )

(post.seab.season.plot<-ggplot(post.plot.data.seasonal.comparisons %>% 
                                 filter(Habitat != "NP") %>% 
                                 mutate(Season = fct_rev(Season)),
                               aes(x = Species, y = SEA_B, fill = Season))+
    geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.8)+
    facet_grid(Slough ~ Habitat, scales = "free")+
    scale_fill_viridis_d(aesthetics = "fill", begin = 0.2, end = 0.88)+
    coord_cartesian(ylim = c(0, 10))+
    theme_bw(base_size = 12)+
    xlab(NULL)+
    ylab(expression("Stomach Content Niche Area - SEA"["b"]))+
    theme(text = element_text(family = "Times", size = 12),
          axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 4),
          #panel.border = element_blank(), 
          #panel.grid.major = element_blank(),
          #panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"),
          axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
          axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_blank(), legend.text = element_text(size = 8),
          legend.key.size = unit(0.75, "line"))
  
)

# ggsave(filename = "post_SEAb_seasonal_comparisons.tiff", plot = post.seab.season.plot,
#        dpi = 600, height = 150, width = 225, units = "mm")

```

## Niche Ellipse Plots

For adults of the two most abundant fishes
```{r}
(iso.niche.ellipse.plot <- ggplot(iso.merge %>% 
                                    filter(size_class %in% c("G.hol2", "L.goo2"),
                                           Habitat != "Sawgrass",
                                           !(Habitat == "NP" & Slough == "TSL")) %>% 
                                    mutate(Species = case_when(
                                      size_class == "G.hol2" ~ "Eastern Mosquitofish (adults)",
                                      size_class == "L.goo2" ~ "Bluefin Killifish (adults)"
                                    )), 
                                  aes(x = d13C, y = d15N, color = Season, fill = Season))+
    stat_conf_ellipse(geom = "polygon", alpha = 0.8)+
    facet_grid(Species ~ Habitat*Slough, scales = "free")+
    labs(y = expression(italic(delta)^15*N~("‰")), x = expression(italic(delta)^13*C~("‰")))+
    scale_fill_viridis_d(end = 0.8)+
    scale_color_viridis_d(end = 0.8)+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
)

# ggsave(filename = "iso_niche_ellipse_plot.tiff", plot = iso.niche.ellipse.plot,
#        height = 150, width = 200, units = "mm", dpi = 300)
```

## Post-Invasion Comparisons

Calculate pairwise comparisons
```{r}
#turn output into a list
SEA.B.list <- as.list(as.data.frame(SEA.B))

#do all pairwise comparisons with function from tRophicPosition package
SEA.B.pairwise <- pairwiseComparisons(SEA.B.list, print = F)
colnames(SEA.B.pairwise) <- rownames(SEA.B.pairwise)
```

###Seasonal Comparisons
#### SRS marsh wet vs dry
```{r}
srs.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

srs.marsh.wet.dry.direct.pairwise <- srs.marsh.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.marsh.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.marsh.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(srs.marsh.wet.dry.direct.pairwise, 
#            file = "srs_marsh_wet_dry_SEAB_prob_diff.xlsx")
```

#### SRS near pond wet vs dry
```{r}
srs.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

srs.np.wet.dry.direct.pairwise <- srs.np.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.np.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(srs.np.wet.dry.direct.pairwise, 
#            file = "srs_np_wet_dry_SEAB_prob_diff.xlsx")
```

#### SRS pond wet vs dry 
```{r}
srs.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

srs.pond.wet.dry.direct.pairwise <- srs.pond.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.pond.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(srs.pond.wet.dry.direct.pairwise, 
#            file = "srs_pond_wet_dry_SEAB_prob_diff.xlsx")
```

#### TSL marsh wet vs dry
```{r}
tsl.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

tsl.marsh.wet.dry.direct.pairwise <- tsl.marsh.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.marsh.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.marsh.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "CHIRON" ~  "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CORIXI" ~ "Corixidae",
      `Size Class` == "EPHEME" ~ "Ephemeroptera",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "GERRID" ~ "Gerridae",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(tsl.marsh.wet.dry.direct.pairwise, 
#            file = "tsl_marsh_wet_dry_SEAB_prob_diff.xlsx")
```

#### TSL near pond wet vs dry
```{r}
tsl.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

tsl.np.wet.dry.direct.pairwise <- tsl.np.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.np.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "CLADOCERA" ~ "Cladocera",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(tsl.np.wet.dry.direct.pairwise, 
#            file = "tsl_np_wet_dry_SEAB_prob_diff.xlsx")
```

#### TSL pond wet vs dry
```{r}
tsl.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

tsl.pond.wet.dry.direct.pairwise <- tsl.pond.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.pond.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
# write.xlsx(tsl.pond.wet.dry.direct.pairwise, 
#            file = "tsl_pond_wet_dry_SEAB_prob_diff.xlsx")
```

#### Combine seasonal pairwise comparisons
```{r}
seasonal.pairwise <- bind_rows(srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,
                               tsl.marsh.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)

write.csv(seasonal.pairwise, 
           "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/season_pairiwise.csv")

#proportion of statistical changes in wet-dry comparisons
sum(seasonal.pairwise$`Dry < Wet`>=0.95 | seasonal.pairwise$`Dry < Wet`<=0.05)/nrow(seasonal.pairwise) 

seasonal.pairwise %>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#break it down per habitat
#marsh
#frequency
sum(srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 | 
      srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
    tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise) %>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#np
#frequency
sum(srs.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 | 
      srs.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
    tsl.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      tsl.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(bind_rows(srs.np.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.np.wet.dry.direct.pairwise, tsl.np.wet.dry.direct.pairwise) %>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#pond
#frequency
sum(srs.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95 | 
      srs.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
    tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise) %>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```

#### Plot seasonal percent change
```{r}
#Format data for plotting
all.season.direct.pairwise <- bind_rows(
  srs.marsh.wet.dry.direct.pairwise,
  srs.np.wet.dry.direct.pairwise,
  srs.pond.wet.dry.direct.pairwise,
  tsl.marsh.wet.dry.direct.pairwise,
  tsl.pond.wet.dry.direct.pairwise #no tsl.np.wet.dry.direct.pairwise
) %>%
  mutate(`Size Class` = case_when(`Size Class` == "C.uro1" ~ "M.uro1",
                                  T ~ `Size Class`)) %>%
  rename(`Wet < Dry` = `Dry < Wet`) %>%
  mutate(
    Trophic_Guild = case_when(
      `Size Class` %in% c ("Ephemeroptera") ~ "Herbivore",
      `Size Class` %in% c("P.lat2", "Amphipoda", "Chironomidae", "Copepoda") ~ "Detritivore",
      `Size Class` %in% c(
        "M.uro1",
        "H.let1",
        "G.hol1",
        "G.hol2",
        "E.suc1",
        "E.suc2",
        "J.flo2",
        "P. alleni",
        "P. fallax",
        "P. paludosus"
      ) ~ "Omnivore",
      `Size Class` %in% c(
        "N.pet1",
        "L.goo1",
        "L.goo2",
        "H.for1",
        "H.for2",
        "F.chr1",
        "F.chr2",
        "B. gravida",
        "Celithimus spp.",
        "Coenagrionidae",
        "Corixidae",
        "E. simplicicollis",
        "E.eve1",
        "E.glo1",
        "Gerridae",
        "Hydrachnidia",
        "L.mar1",
        "L.mic1",
        "P. femoratus"
      ) ~ "Invertivore",
      `Size Class` %in% c(
        "L.pun2",
        "L.pla1",
        "L.gul4",
        "L.gul3",
        "L.gul2",
        "A.nat3",
        "B.bel1",
        "F.con1"
      ) ~ "Mesopredator"
    ),
    Trophic_Score = case_when(
      Trophic_Guild == "Mesopredator" ~ 5,
      Trophic_Guild == "Invertivore" ~ 4,
      Trophic_Guild == "Omnivore" ~ 3,
      Trophic_Guild == "Detritivore" ~ 2,
      Trophic_Guild == "Herbivore" ~ 1
    ),
    `Size Class` = fct_reorder(`Size Class`, Trophic_Score)
  ) %>%
  rename(`Trophic Guild` = Trophic_Guild)

#Generate plots
windowsFonts(Times = windowsFont("Times New Roman"))

(season.percent.change.plot <- ggplot(data = all.season.direct.pairwise, 
                                      aes(y = `Size Class`, x = `Percent Change`, fill = Habitat, 
                                          color = Habitat, shape = Slough))+
    geom_point(aes(size = `Wet < Dry`))+
    scale_shape_manual(values = c(22,24))+
    scale_fill_viridis_d(end = 0.8)+
    scale_color_viridis_d(end = 0.8)+
    scale_size_continuous(breaks = c(0.05, 0.25, 0.75, 0.95))+
    facet_zoom(xlim = c(-100, 100))+
    geom_hline(yintercept = 34.5, linetype = "dashed", color = viridis(n=4, option = "A")[1])+
    geom_hline(yintercept = 15.5, linetype = "dashed", color = viridis(n=4, option = "A")[2])+
    geom_hline(yintercept = 5.5, linetype = "dashed", color = viridis(n=4, option = "A")[3])+
    geom_hline(yintercept = 1.5, linetype = "dashed", color = viridis(n=4, option = "A")[4])+
    theme_bw()+
    theme(text = element_text(family = "Times"),
          axis.text.y = element_text(size = 6))
  
)

# ggsave("stable_isotopes_season_percent_change_plot.tiff", 
#        plot = season.percent.change.plot, 
#        dpi = 600, height = 250, width = 150, units = "mm")

#boxplot for percent change per habitat across all species
(season.percent.change.boxplot <- ggplot(data = all.season.direct.pairwise %>% 
                                           mutate(Habitat = gsub("NP", "Near Pond", Habitat)), 
                                         aes(y = `Percent Change`, x = Habitat))+
    geom_boxplot()+
    facet_zoom(ylim = c(-100, 100))+
    scale_x_discrete(limits = c("Pond", "Near Pond", "Marsh"))+
    theme_bw()+
    theme(text = element_text(family = "Times"))
  
)

all.season.direct.pairwise.long <- all.season.direct.pairwise %>% 
  pivot_longer(cols = c(Wet, Dry), names_to = "Season", values_to = "Niche Area") %>% 
  mutate(Habitat = gsub("NP", "Near Pond", Habitat))


(season.niche.area.habitat.plot <- ggplot(data = all.season.direct.pairwise.long,
                                          aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat)+
    scale_x_discrete(limits = c("Wet", "Dry"))
  #coord_cartesian(ylim = c(0,1))
  
)

season.niche.shifts <- all.season.direct.pairwise.long %>% 
  filter(`Wet < Dry` <= 0.05 | `Wet < Dry` >= 0.95)

season.niche.shifts %>% group_by(Habitat, Season) %>% tally()

(si.season.niche.area.shifts.habitat.plot <- ggplot(data = season.niche.shifts,
                                                 aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    #geom_violin(draw_quantiles = 0.5, alpha = 0.5)+
    #facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat, scales = "free")+
    scale_x_discrete(limits = c("Wet", "Dry"))+
    #coord_cartesian(ylim = c(0,6))+
    ylab(label = expression(Niche[SIA]))+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 8),
          axis.text = element_text(size = 6))
  
)

# ggsave("stable_isotope_seasonal_niche_shifts_per_habitat.tiff", 
#        plot = si.season.niche.area.shifts.habitat.plot, 
#        dpi = 600, height = 150, width = 250, units = "mm")

season.niche.shifts %>% group_by(Habitat, Season, `Trophic Guild`) %>% tally() %>% 
  arrange(Habitat, `Trophic Guild`) %>% print(n = 26)

(si.season.niche.area.shifts.guild.habitat.plot <- ggplot(data = season.niche.shifts,
                                                 aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    #geom_violin(draw_quantiles = 0.5, alpha = 0.5)+
    #facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat*`Trophic Guild`, scales = "free_y")+
    scale_x_discrete(limits = c("Wet", "Dry"))+
    #coord_cartesian(ylim = c(0,6))+
    ylab(label = expression(Niche[SIA]))+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 8),
            axis.text = element_text(size = 6))
  
)

# ggsave("stable_isotope_seasonal_niche_shifts_guild_per_habitat.tiff", 
#        plot = si.season.niche.area.shifts.guild.habitat.plot, 
#        dpi = 600, height = 150, width = 250, units = "mm")

```

###Slough Comparisons
#### Marsh wet season
```{r}
slough.marsh.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.marsh.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.marsh.wet.direct.pairwise <- slough.marsh.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.marsh.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
#write.xlsx(slough.marsh.wet.direct.pairwise, file = "slough_marsh_wet_SEAB_prob_diff.xlsx")
```

#### Near pond wet season
```{r}
slough.np.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.np.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "NP") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.np.wet.direct.pairwise <- slough.np.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.np.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.np.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
#write.xlsx(slough.np.wet.direct.pairwise, file = "slough_np_wet_SEAB_prob_diff.xlsx")
```

#### Pond wet season
```{r}
slough.pond.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.pond.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.pond.wet.direct.pairwise <- slough.pond.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.pond.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
#write.xlsx(slough.pond.wet.direct.pairwise, file = "slough_pond_wet_SEAB_prob_diff.xlsx")
```

#### Marsh dry season
```{r}
slough.marsh.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.marsh.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Dry", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.marsh.dry.direct.pairwise <- slough.marsh.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.marsh.dry.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
#write.xlsx(slough.marsh.dry.direct.pairwise, file = "slough_marsh_dry_SEAB_prob_diff.xlsx")
```

#### Near pond dry season
No comparisons possible because of TSL near ponds drying early

#### Pond dry season
```{r}
slough.pond.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.pond.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Dry", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.pond.dry.direct.pairwise <- slough.pond.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.pond.dry.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
#write.xlsx(slough.pond.dry.direct.pairwise, file = "slough_pond_dry_SEAB_prob_diff.xlsx")
```

#### Combine slough comparisons
```{r}
slough.pairwise <-
  bind_rows(
    slough.marsh.dry.direct.pairwise,
    slough.pond.dry.direct.pairwise,
    slough.marsh.wet.direct.pairwise,
    slough.np.wet.direct.pairwise,
    slough.pond.wet.direct.pairwise
  )

write.csv(slough.pairwise, 
          "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/slough_pairwise.csv",
          row.names = F)

#proportion of statistical changes in slough comparisons
sum(slough.pairwise$`TSL < SRS`>=0.95 | slough.pairwise$`TSL < SRS`<=0.05)/nrow(slough.pairwise) 

slough.pairwise %>% 
  filter(`TSL < SRS` >= 0.95 | `TSL < SRS` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```

### Habitat Comparisons
#### SRS wet marsh vs near pond 
```{r}
srs.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "NP"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

srs.wet.marsh.np.direct.pairwise <- srs.wet.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.wet.marsh.np.direct.pairwise, file = "srs_wet_marsh_np_SEAB_prob_diff.xlsx")
```

#### SRS wet marsh vs pond
```{r}
srs.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

srs.wet.marsh.pond.direct.pairwise <- srs.wet.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.wet.marsh.pond.direct.pairwise, file = "srs_wet_marsh_pond_SEAB_prob_diff.xlsx")
```

#### SRS wet near pond vs pond
```{r}
srs.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("NP", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

srs.wet.np.pond.direct.pairwise <- srs.wet.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.wet.np.pond.direct.pairwise, file = "srs_wet_np_pond_SEAB_prob_diff.xlsx")
```

#### SRS dry marsh vs near pond
```{r}
srs.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "NP"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

srs.dry.marsh.np.direct.pairwise <- srs.dry.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.dry.marsh.np.direct.pairwise, file = "srs_dry_marsh_np_SEAB_prob_diff.xlsx")
```

#### SRS dry marsh vs pond
```{r}
srs.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

srs.dry.marsh.pond.direct.pairwise <- srs.dry.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.dry.marsh.pond.direct.pairwise, file = "srs_dry_marsh_pond_SEAB_prob_diff.xlsx")
```

#### SRS dry near pond vs pond
```{r}
srs.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("NP", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

srs.dry.np.pond.direct.pairwise <- srs.dry.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(srs.dry.np.pond.direct.pairwise, file = "srs_dry_np_pond_SEAB_prob_diff.xlsx")
```

#### TSL wet marsh vs near pond
```{r}
tsl.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "NP"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

tsl.wet.marsh.np.direct.pairwise <- tsl.wet.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COLEOA" ~ "Coleoptera",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "GERRID" ~ "Gerridae",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.wet.marsh.np.direct.pairwise, file = "tsl_wet_marsh_np_SEAB_prob_diff.xlsx")
```

#### TSL wet marsh vs pond
```{r}
tsl.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

tsl.wet.marsh.pond.direct.pairwise <- tsl.wet.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.wet.marsh.pond.direct.pairwise, file = "tsl_wet_marsh_pond_SEAB_prob_diff.xlsx")
```

#### TSL wet near pond vs pond
```{r}
tsl.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("NP", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

tsl.wet.np.pond.direct.pairwise <- tsl.wet.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.wet.np.pond.direct.pairwise, file = "tsl_wet_np_pond_SEAB_prob_diff.xlsx")
```

#### TSL dry marsh vs near pond
```{r}
tsl.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "NP"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

tsl.dry.marsh.np.direct.pairwise <- tsl.dry.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.dry.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.dry.marsh.np.direct.pairwise, file = "tsl_dry_marsh_np_SEAB_prob_diff.xlsx")

```

#### TSL dry marsh vs pond
```{r}
tsl.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

tsl.dry.marsh.pond.direct.pairwise <- tsl.dry.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.dry.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.dry.marsh.pond.direct.pairwise, file = "tsl_dry_marsh_pond_SEAB_prob_diff.xlsx")
```

#### TSL dry near pond vs pond
```{r}
tsl.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("NP", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

tsl.dry.np.pond.direct.pairwise <- tsl.dry.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.dry.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)

#write this out to excel
#write.xlsx(tsl.dry.np.pond.direct.pairwise, file = "tsl_dry_np_pond_SEAB_prob_diff.xlsx")
```

#### Combine habitat comparisons
```{r}
#Marsh-NP
marsh.np.comparisons <- bind_rows(srs.wet.marsh.np.direct.pairwise, srs.dry.marsh.np.direct.pairwise, 
                                  tsl.wet.marsh.np.direct.pairwise, tsl.dry.marsh.np.direct.pairwise)

#proportion of statistical changes in marsh-np comparisons
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)/nrow(marsh.np.comparisons) 

#magnitude
marsh.np.comparisons %>% 
  filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#Marsh-Pond
marsh.pond.comparisons <- bind_rows(srs.wet.marsh.pond.direct.pairwise, srs.dry.marsh.pond.direct.pairwise, 
                                    tsl.wet.marsh.pond.direct.pairwise, tsl.dry.marsh.pond.direct.pairwise)

#proportion of statistical changes in marsh-pond comparisons
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)/nrow(marsh.pond.comparisons) 

#magnitude
marsh.pond.comparisons %>% 
  filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#NP-Pond
np.pond.comparisons <- bind_rows(srs.wet.np.pond.direct.pairwise, srs.dry.np.pond.direct.pairwise, 
                                    tsl.wet.np.pond.direct.pairwise, tsl.dry.np.pond.direct.pairwise)

#proportion of statistical changes in marsh-np comparisons
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)/nrow(np.pond.comparisons) 

#magnitude
np.pond.comparisons %>% 
  filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#combine into a list and export to Excel
habitat.comparisons <- list("Marsh-NP" = marsh.np.comparisons, "Marsh-Pond" = marsh.pond.comparisons, 
                            "NP-Pond" = np.pond.comparisons)

write.xlsx(habitat.comparisons, 
           "Outputs/Stable Isotopes/Trophic Niche/Post-Invasion/habitat_comparisons.xlsx")

#proportion of all habitat comparisons that were statistically different
(
  sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)+
  sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)+
  sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)
)/(
  nrow(marsh.np.comparisons)+nrow(marsh.pond.comparisons)+nrow(np.pond.comparisons)
)

#magnitude
bind_rows(
  marsh.np.comparisons %>% rename(Comparisons = `Marsh < NP`),
  marsh.pond.comparisons %>% rename(Comparisons = `Marsh < Pond`),
  np.pond.comparisons %>% rename(Comparisons = `NP < Pond`)
) %>%  filter(Comparisons >= 0.95 | Comparisons <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```

# Pre- vs Post-Invasion SIBER
## Load data
```{r}
pre.post.invasion.isotopes <- read.csv("Outputs/Stable Isotopes/pre_post_invasion_isotopes.csv")
```

## Format data for SIBER
```{r}
#format data for SIBER
pre.post.siber.data <- pre.post.invasion.isotopes %>% select(-c(Sample_Type)) %>% 
  rename(group = Species, iso1 = d13C, iso2 = d15N, community = Community) %>% 
  select(iso1, iso2, group, community)

#createSiberObject
pre.post.siber<-createSiberObject(pre.post.siber.data)

#write out samples sizes table
write.csv(pre.post.siber[["sample.sizes"]] %>% t(), file = "Outputs/Stable Isotopes/siber_pre_post_iso_n_table.csv")

# Create lists of plotting arguments to be passed onwards to each 
# of the three plotting functions.
community.hulls.args <- list(col = 1, lty = 1, lwd = 1)
group.ellipses.args  <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)
group.hull.args      <- list(lty = 2, col = "grey20")

par(mfrow=c(1,1))
plotSiberObject(pre.post.siber,
                ax.pad = 2, 
                hulls = F, community.hulls.args, 
                ellipses = T, group.ellipses.args,
                group.hulls = T, group.hull.args,
                bty = "L",
                iso.order = c(1,2),
                xlab = expression(paste(delta^{13}, "C (\u2030)")),
                ylab = expression(paste(delta^{15}, "N (\u2030)"))
)
```

## Calculate TA, SEA, SEAc
```{r}
#PALPAL post-invasion have some d13C values that seem like they're incorrect
pre.post.ML <- groupMetricsML(pre.post.siber)
print(pre.post.ML)
#write.csv(t(pre.post.ML), file = "pre_post_ML.csv")
```

## Run SIBER model
```{r}
#use the same model parameters
# fit the ellipses which uses an Inverse Wishart prior
# on the covariance matrix Sigma, and a vague normal prior on the 
# means. Fitting is via the JAGS method.
pre.post.ellipses.posterior <- siberMVN(pre.post.siber, parms, priors)

#calculate posterior estimates of ellipses for all groups
pre.post.SEA.B<-siberEllipses(pre.post.ellipses.posterior)
colnames(pre.post.SEA.B)<-names(pre.post.ellipses.posterior)
```

### Extract Output
```{r}
#calculate credibility intervals
cr.p<-c(0.5, 0.95, 0.99) #vector of desired quantiles

#call hdrcde_hdr using lapply()
pre.post.SEA.B.credibles<-lapply(
  as.data.frame(pre.post.SEA.B), 
  function(x,...){tmp<-hdrcde::hdr(x)$hdr},
  prob = cr.p)

#match up group names
names(pre.post.SEA.B.credibles)<-names(pre.post.ellipses.posterior)

#this carries through the row names from matrices in SEA.B.credibles
#they don't turn out all that clean though, so adding a column later with the same info
pre.post.y <- do.call(function(...) {
  tmp <- plyr::rbind.fill.matrix(...)
  rownames(tmp) <- sapply(pre.post.SEA.B.credibles, function(i) {
    rownames(i)
  })
  return(tmp)
}, pre.post.SEA.B.credibles)

pre.post.y

#object with cleaned-up row names
pre.post.interval<-rep(c("99%", "95%", "50%"), 32)

#replicated each name three times in sequence to match up with new data frame
pre.post.seab.names<-data.frame(rep(names(pre.post.SEA.B.credibles), each = 3))
colnames(pre.post.seab.names)<-"names"

#seab.names<-separate(seab.names, col = names, into = c('hab.season', 'spp'), extra = "merge")

#combine names with new data frame
pre.post.seab.cred.matrix<-data.frame(pre.post.seab.names, pre.post.interval, pre.post.y)
colnames(pre.post.seab.cred.matrix)<-c(colnames(pre.post.seab.names), "interval", "lower", "upper")

#write out to a csv
write.csv(pre.post.seab.cred.matrix, 
          file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_credibles.csv", 
          row.names = F)

# do similar to get the modes, taking care to pick up multimodal posterior
# distributions if present
pre.post.SEA.B.modes<-lapply(
  as.data.frame(pre.post.SEA.B), 
  function(x,...){tmp<-hdrcde::hdr(x)$mode},
  prob = cr.p, all.modes=T)

#need to do the same as for the credible intervals to get the colnames to be
#the names not the numbers
write.csv(pre.post.SEA.B.modes, file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")

#read back in
pre.post.SEA.B.modes.matrix<-read.csv(
  file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv", 
                                      header = T, row.names = 1)

#change colnames
colnames(pre.post.SEA.B.modes.matrix)<-names(pre.post.ellipses.posterior)
#transpose this matrix
pre.post.SEA.B.modes.matrix.t<-t(pre.post.SEA.B.modes.matrix) %>% as.data.frame()
#change the name of what is now a single column to mode
colnames(pre.post.SEA.B.modes.matrix.t)<-"Mode"
#write out the final csv over the previous mode csv
write.csv(pre.post.SEA.B.modes.matrix.t, 
          file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")

# #test convergence for all ellipses
# pre.post.all.files <- dir(parms$save.dir, full.names = T)
# 
# #find which onces are jags model files
# pre.post.model.files <- pre.post.all.files[grep("jags_output", pre.post.all.files)]
# 
# #test convergence 
# do.this <- 1 #this number indicates which file, in order, is being looked at
# load(pre.post.model.files[do.this])
# 
# gelman.diag(output, multivariate = F)
# gelman.plot(output, auto.layout = F)
```

### Pre- vs Post-Invasion Comparisons
Calculate pairwise comparisons
```{r}
#turn the output into a list
pre.post.SEA.B.list <- as.list(as.data.frame(pre.post.SEA.B))

#use the pairwiseComparisons function from tRophicPosition package
pre.post.pairwise <- pairwiseComparisons(pre.post.SEA.B.list, print = F)
colnames(pre.post.pairwise) <- rownames(pre.post.pairwise)
```

Format data to have only pre- vs post-invasion comparisons
```{r}
#reformat data to have only pre-invasion and post-invasion comparisons
pre.post.direct.pairwise <-
  pre.post.pairwise %>% as.data.frame() %>%
  rownames_to_column(var = "Invasion_Status_Spp") %>%
  filter(grepl("Pre", Invasion_Status_Spp)) %>%
  separate(
    Invasion_Status_Spp,
    into = c("Invasion_Status", "Species"),
    sep = "[//.]"
  ) %>%
  arrange(Species) %>%
  unite("Invasion_Status_Spp", c(Invasion_Status, Species)) %>%
  column_to_rownames(var = "Invasion_Status_Spp") %>%
  t() %>% as.data.frame() %>%
  rownames_to_column(var = "Invasion_Status_Spp") %>%
  filter(grepl("Post", Invasion_Status_Spp)) %>%
  separate(
    Invasion_Status_Spp,
    into = c("Invasion_Status", "Species"),
    sep = "[//.]"
  ) %>%
  arrange(Species) %>%
  unite("Invasion_Status_Spp", c(Invasion_Status, Species)) %>%
  column_to_rownames(var = "Invasion_Status_Spp") %>%
  t() %>% as.data.frame()

#format modes to bind with pairwise probs
pre.post.modes.wide <- pre.post.SEA.B.modes.matrix.t %>%
  rownames_to_column(var = "Invasion_Status_Spp") %>%
  separate(
    Invasion_Status_Spp,
    into = c("Invasion_Status", "Species"),
    sep = "[//.]"
  ) %>%
  pivot_wider(id_cols = Species,
              names_from = Invasion_Status,
              values_from = Mode) %>%
  filter(!Species %in% c("Green Algae", "FLOC"))

pre.post.direct.spp.pairwise <-
  pre.post.direct.pairwise %>% as.matrix() %>%
  diag() %>% as.data.frame() %>%
  mutate(Species = rownames(pre.post.direct.pairwise)) %>%
  separate(Species,
           into = c("Invasion_Status", "Species"),
           sep = "_") %>%
  filter(!Species %in% c("Green Algae", "FLOC")) %>%
  merge(., pre.post.modes.wide, by = "Species") %>%
  select(Species, contains("Invasion"), everything()) %>% select(-c(Invasion_Status)) %>%
  rename(`Pre < Post` = ".") %>%
  mutate(
    #`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Pre < Post` >= 0.95 ~ "Decrease",
      `Pre < Post` < 0.05 ~ "Increase",
      `Pre < Post` > 0.05 | `Pre < Post` < 0.95 ~ "No Change"
    ),
    `Percent Change` = ((`Post-Invasion` - `Pre-Invasion`) / `Pre-Invasion`) *
      100,
    Species = case_when(
      Species == "AMENAT" ~ "A. natalis",
      Species == "ANISOP" ~ "Anisoptera",
      Species == "BELSPP" ~ "Belostoma spp.",
      Species == "CICURO" ~ "C. urophthalmus",
      Species == "ERISUC" ~ "E. sucetta",
      Species == "FUNCHR" ~ "F. chrysotus",
      Species == "GAMHOL" ~ "G. holbrooki",
      Species == "HETFOR" ~ "H. formosa",
      Species == "LEPMAC" ~ "L. macrochirus",
      Species == "LEPMIC" ~ "L. microlophus",
      Species == "LEPPUN" ~ "L. punctatus",
      Species == "PALPAL" ~ "P. paludosus",
      Species == "POELAT" ~ "P. latipinna",
      Species == "PROFAL" ~ "P. fallax"
    )
  ) %>%
  mutate(across(where(is.numeric), round, digits = 2))

#write this out to excel
write.csv(pre.post.direct.spp.pairwise, 
           file = "Outputs/Stable Isotopes/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_prob_diff.csv")

#proportion of statistical changes in pre-post comparisons
sum(
  pre.post.direct.spp.pairwise$`Pre < Post` >= 0.95 |
    pre.post.direct.spp.pairwise$`Pre < Post` <= 0.05
) / nrow(pre.post.direct.spp.pairwise)

```

### Plot pre- vs post-invasion shifts
```{r}
#reformat data to be able to make boxplots or violoin plots
pre.post.plot.data <- pre.post.SEA.B %>% as.data.frame() %>% pivot_longer(everything(), names_to = c("Invasion Status", "Species"), 
                                                         names_sep = "[//.]", values_to = "SEA_B") %>% 
  filter(!Species %in% c("Green Algae", "FLOC", "PROFAL")) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "ANISOP" ~ "Anisoptera",
    Species == "BELSPP" ~ "Belostoma spp.",
    Species == "CICURO" ~ "C. urophthalmus",
    Species =="ERISUC" ~ "E. sucetta", 
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "HETFOR" ~ "H. formosa",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMIC" ~ "L. microlophus",
    Species == "LEPPUN" ~ "L. punctatus",
    Species == "PALPAL" ~ "P. paludosus",
    Species == "POELAT" ~ "P. latipinna"))

#plot these data
windowsFonts(Times = windowsFont("Times New Roman"))

(pre.post.seab<-ggplot(pre.post.plot.data %>% 
                         mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(`Invasion Status`))), 
                       aes(x = Species, y = SEA_B, fill = `Invasion Status`))+
  #geom_violin()+
  geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1)+
  #facet_wrap(~Species)+
  scale_color_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+
  scale_y_continuous(limits = c(0, 22))+
  theme_bw(base_size = 8)+
  xlab(NULL)+
  ylab("Niche Area")+
  annotate("text", x = 4, y = 3.75, label = "*", size = 2)+
  annotate("text", x = 5, y = 8.5, label = "*", size = 2)+
  annotate("text", x = 6, y = 8.25, label = "*", size = 2)+
  annotate("text", x = 7, y = 21, label = "*", size = 2)+
  annotate("text", x = 9, y = 1.75, label = "*", size = 2)+
  annotate("text", x = 10, y = 3, label = "*", size = 2)+
  annotate("text", x = 13, y = 11, label = "*", size = 2)+
  theme(text = element_text(family = "Times", size = 5),
        axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
        panel.border = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
        axis.ticks.x = element_line(size = 0.05),
        axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
        axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
        legend.title = element_blank(), legend.text = element_text(size = 4),
        legend.key.size = unit(0.75, "line"))

)

#ggsave(filename = "pre_post_SEAb.tiff", plot = pre.post.seab, dpi = 600, width = 84, height = 60, units = "mm")
```

